Long Short-term Memory Approach Research Articles

Long short-term memory (LSTM) neural networks for predicting dynamic responses and application in piezoelectric energy harvesting

Long Short-Time Memory (LSTM) deep neural networks are capable of learning order dependence in sequence problems and capturing long-term, non-linear temporal dependencies between the input and out of a system. With the long-term vision to model dynamical systems to which analytical or numerical methods are impossible or difficult to apply, this paper presents a study of modeling system dynamics and predicting responses using the LSTM networks, which have demonstrated excellent capability in predicting single-mode responses in a prior study. However, the LSTM network exhibits difficulties in modeling and predicting multi-mode responses accurately. To resolve the multi-mode issue, this paper presents an approach that obtains an equivalent network consisting of a set of sub-networks learned on isolated modes, and demonstrates its effectiveness on a simulated 2-degree-of-freedom mass-spring-damper system of nonlinear Duffing springs. The second part of the paper is focused on the application of the proposed approach in piezoelectric energy harvesting. Experiments are conducted on a harvester subjected to random base-motion excitation and exhibiting nonlinearity in its multi-mode response. Both the direct and mode-separation LSTM modeling approaches are applied to predict the output voltage given a random base-motion excitation. The mode-separation approach outperforms the direct approach significantly, and yields an excellent match between the actual and predicted responses. Specifically, for a test electrical voltage response of RMS value 0.2241 V, the difference between the actual test and predicted responses by using the mode-separation approach has an RMS value of 0.0504 V, compared to 0.1645 V obtained by using the direct LSTM approach. It is also much lower than the RMS value of 0.1835 V obtained by using the attention-based LSTM network, another comparison method. Leveraging a deep learning strategy, the validated approach opens up opportunities for accurately modeling energy harvesting systems of high complexities and/or strong nonlinearities.

Intelligent intrusion detection through deep autoencoder and stacked long short-term memory

In the realm of network intrusion detection, the escalating complexity and diversity of cyber threats necessitate innovative approaches to enhance detection accuracy. This study introduces an integrated solution leveraging deep learning techniques for improved intrusion detection. The proposed framework consists on a deep autoencoder for feature extraction, and a stacked long short-term memory (LSTM) network ensemble for classification. The deep autoencoder compresses raw network data, extracting salient features and mitigating noise. Subsequently, the stacked LSTM ensemble captures intricate temporal dependencies, correcting anomaly detection precision. Experiments conducted on the UNSW-NB15 dataset, and a benchmark in intrusion detection validate the effectiveness of the approach. The solution achieves an accuracy of 90.59%, with precision, recall, and F1-Score metrics reaching 90.65, 90.59, and 90.57, respectively. Notably, the framework outperforms standalone models and demonstrates the advantage of synergizing deep autoencoder-driven feature extraction with the stacked LSTM ensemble. Furthermore, a binary classification experiment attains an accuracy of about 90.59%, surpassing the multiclass classification and affirming the model's potential for binary threat identification. Comparative analyses highlight the pivotal role of feature extraction, while experimentation illustrates the enhancement achieved by incorporating the synergistic deep autoencoder-Stacked LSTM approach.

Evaluating the efficacy of univariate LSTM approach for COVID-19 data prediction in Indonesia

The coronavirus disease 2019 (COVID-19) pandemic, originating in 2020, has emerged as a critical global issue due to its rapid and widespread transmission. Indonesia, among the affected nations, has taken measures to address the situation, including the development of a deep learning model for predicting future COVID-19 infection and spread. This predictive tool serves as a valuable reference for the government and stakeholders, aiding them in making informed decisions and implementing appropriate measures to contain the virus. The deep learning model employs the long short-term memory (LSTM) algorithm, chosen for its ability to recognize temporal patterns in the country’s COVID-19 data. The model creation process involves data collection, preprocessing, model architecture planning, modeling, training, and evaluation. Two LSTM models were developed: a univariate and a multivariate model. Following thorough training and evaluation, the univariate model emerged as the superior choice, boasting evaluation metrics of 16.72 for mean absolute percentage error (MAPE) and 66.36 for root mean squared error (RMSE). This model was then deployed on a publicly accessible website, presenting visualizations of past COVID-19 data and predictions of future cases through line graphs. This user-friendly platform enables the public to access and analyze the data easily.

A dynamic prediction model of landslide displacement based on VMD–SSO–LSTM approach

Addressing the limitations of existing landslide displacement prediction models in capturing the dynamic characteristics of data changes, this study introduces a novel dynamic displacement prediction model for landslides. The proposed method combines Variational Mode Decomposition (VMD) with Sparrow Search Optimization (SSO) and Long Short-Term Memory (LSTM) techniques to formulate a comprehensive VMD–SSO–LSTM model. Through the application of VMD, the method dissects cumulative displacement and rainfall data, thereby extracting distinct components such as trend, periodicity, and fluctuation components for displacement, as well as low-frequency and high-frequency components for rainfall. Furthermore, leveraging Gray Correlational Analysis, the interrelationships between the periodic component of displacement and the low-frequency component of rainfall, as well as the fluctuation component of displacement and the high-frequency component of rainfall, are established. Building upon this foundation, the SSO–LSTM model dynamically predicts the interrelated displacement components, synthesizing the predicted values of each component to generate real-time dynamic forecasts. Simulation results underscore the effectiveness of the proposed VMD–SSO–LSTM model, indicating root-mean-square error (RMSE) and mean absolute percentage error (MAPE) values of 1.2329 mm and 0.1624%, respectively, along with a goodness of fit (R2) of 0.9969. In comparison to both back propagation (BP) prediction model and LSTM prediction model, the VMD–SSO–LSTM model exhibits heightened predictive accuracy.

Deep Reinforcement Learning Lane-Changing Decision Algorithm for Intelligent Vehicles Combining LSTM Trajectory Prediction

Intelligent decisions for autonomous lane-changing in vehicles have consistently been a focal point of research in the industry. Traditional lane-changing algorithms, which rely on predefined rules, are ill-suited for the complexities and variabilities of real-world road conditions. In this study, we propose an algorithm that leverages the deep deterministic policy gradient (DDPG) reinforcement learning, integrated with a long short-term memory (LSTM) trajectory prediction model, termed as LSTM-DDPG. In the proposed LSTM-DDPG model, the LSTM state module transforms the observed values from the observation module into a state representation, which then serves as a direct input to the DDPG actor network. Meanwhile, the LSTM prediction module translates the historical trajectory coordinates of nearby vehicles into a word-embedding vector via a fully connected layer, thus providing predicted trajectory information for surrounding vehicles. This integrated LSTM approach considers the potential influence of nearby vehicles on the lane-changing decisions of the subject vehicle. Furthermore, our study emphasizes the safety, efficiency, and comfort of the lane-changing process. Accordingly, we designed a reward and penalty function for the LSTM-DDPG algorithm and determined the optimal network structure parameters. The algorithm was then tested on a simulation platform built with MATLAB/Simulink. Our findings indicate that the LSTM-DDPG model offers a more realistic representation of traffic scenarios involving vehicle interactions. When compared to the traditional DDPG algorithm, the LSTM-DDPG achieved a 7.4% increase in average single-step rewards after normalization, underscoring its superior performance in enhancing lane-changing safety and efficiency. This research provides new ideas for advanced lane-changing decisions in autonomous vehicles.

A Novel Framework to Forecast COVID-19 Incidence Based on Google Trends Search Data

The global outbreak of coronavirus disease 2019 (COVID-19) has spread to more than 200 countries worldwide, leading to severe health and socioeconomic consequences. As such, the topic of monitoring and predicting epidemics has been attracting a lot of interest. Previous work reported search volumes from Google Trends are beneficial in decoding influenza dynamics, implying its potential for COVID-19 prediction. Therefore, a predictive model using the Wiener methods was built based on epidemic-related search queries from Google Trends, along with climate variables, aiming to forecast the dynamics of the weekly COVID-19 incidence in Washington, DC, USA. The Wiener model, which shares the merits of interpretability, low computation costs, and adaptation to nonlinear fluctuations, was used in this study. Models with multiple sets of features were constructed and further optimized by the highest weight selecting strategy. Furthermore, comparisons to the other two commonly used prediction models based on the autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) were also performed. Our results showed the predicted COVID-19 trends significantly correlated with the actual (rho <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> 0.88, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p $</tex-math> </inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt;$</tex-math> </inline-formula> 0.0001), outperforming those with ARIMA and LSTM approaches, indicating Google Trends data as a useful tool in terms of COVID-19 prediction. Also, the model using 20 search queries with the highest weighting outperformed all other models, supporting the highest weight feature selection as a feasible criterion. Google Trends search query data can be used to forecast the outbreak of COVID-19, which might assist health policymakers to allocate health care resources and taking preventive strategies.

An Adam based CNN and LSTM approach for sign language recognition in real time for deaf people

Hand gestures and sign language are crucial modes of communication for deaf individuals. Since most people can't understand sign language, it's hard for a mute and an average person to talk to each other. Because of technological progress, computer vision and deep learning can now be used to count. This paper shows two ways to use deep knowledge to recognize sign language. These methods help regular people understand sign language and improve their communication. Based on American sign language (ASL), two separate datasets have been constructed; the first has 26 signs, and the other contains three significant symbols with the crucial sequence of frames or videos for regular communication. This study looks at three different models: the improved ResNet-based convolutional neural network (CNN), the long short-term memory (LSTM), and the gated recurrent unit (GRU). The first dataset is used to fit and assess the CNN model. With the adaptive moment estimation (Adam) optimizer, CNN obtains an accuracy of 89.07%. In contrast, the second dataset is given to LSTM and GRU and a comparison has been conducted. LSTM does better than GRU in all classes. LSTM has a 94.3% accuracy, while GRU only manages 79.3%. Our preliminary models' real-time performance is also highlighted.

Stock movement prediction: A multi‐input LSTM approach

AbstractGenerally, the nonlinear and non‐stationary financial time series becomes an obstacle in the process of stock movement prediction. But the recent theories of machine learning and deep learning have provided with some new solutions. Based on LSTM (long short‐term memory), we propose a hybrid model of wavelet transform (WT) and multi‐input LSTM to predict the trend of SSE composite index. It can mine valid data in time series and support different types of data as input. The whole model is divided into two stages. In the first stage, we adopt the level 1 decomposition with db4 mother wavelet to eliminate noise. In the second stage, combinative and qualitative analysis was made base on the data from Chinese stock market, US stock market, and technical indicators as input. According to the result, the proposed model, with the accuracy of 72.19%, performs better than single‐input LSTM, decision tree, random forest, Support Vector Machine (SVM), and XGBoost.

Deep Learning‐Based Regional Ionospheric Total Electron Content Prediction—Long Short‐Term Memory (LSTM) and Convolutional LSTM Approach

AbstractThis study evaluates the performance of deep learning approach in the prediction of the ionospheric total electron content (TEC) during magnetically quiet periods. Two deep learning techniques, long short‐term memory (LSTM) and convolutional LSTM (ConvLSTM), are employed to predict TEC values 24 hr ahead in the vicinity of the Korean Peninsula (26.5°–40°N, 121°–134.5°E). The LSTM method predicts TEC at a single point based on time series of data at that point, whereas the ConvLSTM method simultaneously predicts TEC values at multiple points using spatiotemporal distribution of TEC. Both the LSTM and ConvLSTM models are trained using the complete regional TEC maps reconstructed by applying the Deep Convolutional Generative Adversarial Network–Poisson Blending (DCGAN‐PB) method to observed TEC data. The training period spans from 2002 to 2018, and the model performance is evaluated using 2019 data. Our results show that the ConvLSTM method outperforms the LSTM method, generating more reliable TEC maps with smaller root mean square errors when compared to the ground truth (DCGAN‐PB TEC maps). This outcome indicates that deep learning models can improve the prediction accuracy of TEC at a specific point by taking into account spatial information of TEC. We conclude that ConvLSTM is a reliable and efficient approach for the prompt ionospheric prediction.

Deep CNN and LSTM Approaches for Efficient Workload Prediction in Cloud Environment

In the dynamic landscape of cloud computing, efficient resource allocation and workload prediction are paramount for optimizing infrastructure utilization, cost management, and overall service quality. As a result, an efficient resource management approach involves pooling available resources among many customers in a manner that considers their energy usage, SLAs, and forecasting accuracy all at once. Based on the performance analysis results, it is determined that integrating these techniques would result in the most efficient and adaptable cloud data centre. We proposed a Model based on Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) for handling SLAs in the cloud from both the perspective of consumers and service providers. Subsequently, we delve into the methodology of applying deep CNN and LSTM models to the problem of workload prediction in cloud environments. This methodology encompasses data preprocessing, model architecture, training parameters, and the choice of performance metrics. To anticipate CPU consumption from time series data and detect SLA violations, we suggested a DCNN-LSTM model. The accuracy prediction, energy usage, CPU use, and Service Level Agreement monitoring are all a part of this model. The proposed method is effective in helping cloud providers cut down on service violations and associated fines. Regarding the composite metric of Energy SLA Violation, which assesses the combined aspects of energy use and adherence to Service Level Agreements (SLAs), DCNN-LSTM surpasses ARIMA-LSTM, CNN, LSTM, and ARIMA 6.8%, 10.88%, 16.6%, and 22.4%, respectively.

Penerapan Metode Recurrent Neural Network dengan Pendekatan Long Short-Term Memory (LSTM) Untuk Prediksi Harga Saham

Investing in shares is now increasingly popular and growing rapidly in Indonesia. By investing in shares, investors will get quite fast and large profits in a fairly short time. Investors need to analyze previous stock movements as a form of investment strategy to get maximum investment results. Many techniques have been applied to predict stock prices, one of which uses techniques in Deep Learning such as Recurrent Neural Network. In this research, research was conducted on stock price predictions using the Recurrent Neural Network method with the Long-Short Term Memory (LSTM) approach on BBNI stock data. The research only uses close data or data about daily closing stock prices. In designing LSTM there are several things that are configured such as dropout size, density, activation function, and number of neurons used, and in the training process one of the optimizers provided by the Keras framework is used, namely the ADAM (Adaptive Moment Estimation) optimizer. The test scenario is carried out using a number of epochs of 10 and 20 with a batch size of 32. The test results will produce MAE and MAPE values, where the lower the MAE and MAPE values, the better the model's performance in making accurate predictions. The test results using epoch 10 got an MAE value of 0.0286 and a MAPE value of 0.0488, while the test results using epoch 20 got an MAE value of 0.0150 and a MAPE value of 0.0257.

Forecasting electricity consumption by LSTM neural network

Relevance. The need to enhance the precision of electricity consumption forecasting for improving energy efficiency and, consequently, enhancing the competitiveness of manufactured products by reducing the proportion of electricity costs in their total cost. When determining forecast indicators of electricity consumption by industrial enterprises, it is important to apply contemporary high-precision forecasting methods. Only 20–30 forecasting methods of the 150 existing ones are actively implemented in practice. An examination of prevailing forecasting methodologies used by industrial enterprises reveals that they are mainly based either on expert assessments of electricity volumes or on accounting for specific electricity consumption (per unit of product manufactured). Aim. To elevate the accuracy of electricity consumption forecasting at industrial enterprises by using artificial intelligence methods, specifically, artificial neural network techniques, including the Long-Short Term Memory (LSTM) approach. Methods. When developing the forecasting model, artificial neural network techniques were adopted, with a particular emphasis on the Long-Short Term Memory (LSTM) method. For primary data processing, Gaussian distribution principles and normalization/scaling techniques were applied. Results. Substantiated computationally by applying the proposed model based on the artificial neural network technique for forecasting electricity consumption of industrial enterprises. A significant advantage of this method is its capability for learning and adaptability to forecasting. Real-time computations demonstrate its successful implementation, attributed primarily to appropriate selection of input layers and mitigation of random variables.

Classifying Bengali Newspaper Headlines with Advanced Deep Learning Models: LSTM, Bi-LSTM, and Bi-GRU Approaches

Reading newspapers is beneficial for people of all ages and the global community. The enjoyment of gathering diverse data from various sources adds to the overall experience. To enhance specificity in Bengali news headlines, recognizing the news genre becomes crucial. Recognizing the genre of the news, it is a very challenging task in Bengali Text Classification with the help of AI. A very few research works is done on Bengali News headline classification and we have done a model to provide a solution to the addressed issue. Due to the continuous change of the structure of the news headlines, we have employed a neural network adoption connection to our methodology experiment on a mixture of primary and secondary dataset. Achieving significant results, we implemented a Bengali dataset in Multi Classification using Long-Short Term Memory (LSTM), Bi- Long-Short Term Memory (Bi-LSTM), and Bi-Gated Recurrent Unit (Bi-GRU). The dataset is established by aggregating news headlines from various Bengali news portals and websites, showcasing robust categorization performance in the end product. Six categories were employed for the classification of Bengali newspaper headlines. The Bi-LSTM Model emerged with the highest training accuracy at 97.96% and the lowest validation accuracy at 77.91%. Furthermore, it demonstrated enhanced sensitivity and specificity.

An Empirical Modal Decomposition-Improved Whale Optimization Algorithm-Long Short-Term Memory Hybrid Model for Monitoring and Predicting Water Quality Parameters

Prediction of water quality parameters is a significant aspect of contemporary green development and ecological restoration. However, the conventional water quality prediction models have limited accuracy and poor generalization capability. This study aims to develop a dependable prediction model for ammonia nitrogen concentration in water quality parameters. Based on the characteristics of the long-term dependence of water quality parameters, the unique memory ability of the Long Short-Term Memory (LSTM) neural network was utilized to predict water quality parameters. To improve the accuracy of the LSTM prediction model, the ammonia nitrogen data were decomposed using Empirical Modal Decomposition (EMD), and then the parameters of the LSTM model were optimized using the Improved Whale Optimization Algorithm (IWOA), and a combined prediction model based on EMD-IWOA-LSTM was proposed. The study outcomes demonstrate that EMD-IWOA-LSTM displays improved prediction accuracy with reduced RootMean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) in comparison to the LSTM and IWOA-LSTM approaches. These research findings better enable the monitoring and prediction of water quality parameters, offering a novel approach to preventing water pollution rather than merely treating it afterwards.

Prediction of ultimate bearing capacity of shallow foundations on cohesionless soil using hybrid LSTM and RVM approaches: An extended investigation of multicollinearity

This research presents the optimum performance model for predicting the shallow foundation ultimate bearing capacity (UBC). Twenty-one models are employed, trained, tested, and compared to find the best architecture computational model. Thirteen performance metrics, including three new index metrics, are used to calculate and analyze the model's performance. Five kernels, linear, polynomial, gaussian, laplacian, and exponential, have been implemented in RVM (mentioned by SRVM). These RVM models have been optimized by each particle swarm optimization (PSO) and genetic (GA) algorithm. Moreover, the kernel function of the higher-performance SRVM model has been used as kernel 1 to develop dual (parallel) kernel function-based RVM (mentioned by HRVM) models. Thus, four combinations have developed four HRVM models optimized by each GA and PSO algorithm. The Adam, root mean square propagation, and stochastic gradient descent with momentum algorithms have optimized long short-term memory (LSTM) models. The analysis of performance metrics reveals that (i) the GA-optimized laplacian SRVM model UBC3 has performed better than the PSO-optimized exponential SRVM model UBC10, (ii) PSO-optimized dual kernel-based HRVM model UBC18 has attained higher performance than GA-optimized dual kernel-based HRVM model UBC14, i.e., 0.9985. Adam-optimized model UBC19 based on the LSTM approach has gained higher performance (R = 0.9999) and the least residuals (RMSE = 5.9132 kPa, MAE = 3.7037 kPa, MAPE = 2.5314 kPa, and WMAPE = 0.0118 kPa). Also, model UBC19 has outperformed models UBC3, UBC10, UBC14, and UBC18 and published models available in the literature with higher index metrics. This study reveals that the HRVM models are less affected by multicollinearity.

IoT enhanced metaheuristics with deep transfer learning based robust crop pest recognition and classification

This study addresses the pressing need to enhance food production and quality by tackling the challenge of crop pest recognition. Traditional methods for identifying crop pests are time-consuming and reliant on highly trained experts. To overcome these limitations, this research introduces the Enhanced Metaheuristics with Deep Transfer Learning-based Robust Crop Pest Recognition and Classification (EMDTL-CPRC) model. The EMDTL-CPRC model incorporates several innovative elements, including contrast enhancement to improve image quality, the integration of the Whale Optimization Algorithm (WOA) with the Residual Network (ResNet) approach as a feature extractor, and the utilization of the Long Short-Term Memory (LSTM) method for pest recognition. Furthermore, to enhance the efficacy of the LSTM approach, a Chaotic Salp Swarm Algorithm (CSSA) is incorporated. The simulation results, based on a comprehensive crop pest dataset, demonstrate the superior performance of the EMDTL-CPRC model in crop pest classification. By enhancing pest recognition accuracy and streamlining the process, this research contributes to the overall goal of improving agricultural crop productivity and ensuring the supply of healthy food.

DETECTION MODEL FOR FAKE NEWS ON COVID-19 IN INDONESIA

Today, fake information has become a significant problem, exacerbated by the acceleration of access to information. The spread of fake information has a dangerous impact, especially regarding global health issues, for example COVID-19. People can access various resources to obtain information, including online sites and social media. One of the methods to control the spread of false information is detecting hoaxes. Many methods have been developed to identify hoaxes; most previous studies have focused on developing hoax detection methods using data from a single source in English. The present study is carried out to detect fake news in Indonesian language using multiple data sources, including traditional and social media in the context of COVID-19. The study uses Long Short-Term Memory (LSTM) and the Robustly Optimised Bidirectional Encoder Representations from Transformers Pre-Training Approach (RoBERTa). The LSTM approach is used to develop four different architectures that varied based on: (1) the use of text-only versus the use of both title and text; (2) the number of LSTM and dense layers; and (3) the activation function. The LSTM model with text-only data, a single LSTM layer and two dense layers, outperformed other LSTM architectures, achieving the highest accuracy of 92.17%. The LSTM models require a considerably short training time of 23–27 minutes for 3,847 articles and has a detection time of 3.8–4.1 ms per article. The RoBERTa classifiers outperformed all LSTM models with an accuracy of over 97% and a significantly better training time, with a margin of more than 50% compared to LSTM classifiers, although it had a slightly longer test time. Both LSTM and RoBERTa models outperformed the Naïve Bayes and SVM benchmark methods in terms of accuracy, precision, and recall. Therefore, this study shows that both LSTM and RoBERTa methods are reliable and can be reasonably implemented for real-time fake news detection.

Encoder-decoder models in sequence-to-sequence learning: A survey of RNN and LSTM approaches

In today's information age, from natural language processing to audio signal processing, from time series analysis to machine translation, the application of sequence data involves various fields. Encoder-decoder models, as a powerful approach to sequence modeling, have attracted extensive attention and research. This review paper aims to explore encoder-decoder models, focusing on the principles and operational steps of recurrent neural networks (RNN) and long short-term memory (LSTM), aiming to provide researchers and practitioners with a deep understanding of the fundamentals and applications of these models Condition. Through the analysis and summary of relevant literature, this study reveals the advantages of RNN and LSTM in sequence data processing; RNN structure is simple and effective, can process sequence input and output of any length, and can capture the time dependence in sequence data. But there is a problem of gradient disappearance, which makes it difficult to deal with long-term dependencies. To solve this problem, the LSTM model introduces different gating mechanisms, which effectively solve the gradient problem while better capturing long-term dependencies. Through the review of the principles and applications of the two, it provides a useful reference for further research and application.

BivalveNet: A hybrid deep neural network for common cockle (Cerastoderma edule) geographical traceability based on shell image analysis

Bivalve traceability is a major concern. It is of utmost importance to develop tools that allow providing important information to the consumer, not only on the origin of the product but also on its sustainability and safety, due to the harvest restrictions imposed by regulatory entities. This study evaluated the application of computer vision machine learning technologies for efficiently discriminating cockle harvesting origin based on shell geometric and morphometric analysis, improving the traceability methodologies in these organisms, and highlighting the potential of these low-cost techniques as a reliable traceability tool. Thirty Cerastoderma edule samples were collected along the five locations in Atlantic West and South Portuguese coast with individual images processed using lazysnapping segmentation, spectro-textural-morphological phenotype extraction, and feature selection through hybrid Principal Component Analysis and Neighborhood Component Analysis which resulted in R, a*, b*, entropy, and diameter. Three approaches of traceability models were developed and tested: pre-trained networks (EfficientNet-Bo, ResNet101, MobileNetV2, InceptionV3) with numerical inputs (Approach 1), image-based pre-trained networks (Approach 2), and hybrid deep neural networks of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) (Approach 3). Based on the test results, Approach 3 with GRU-LSTM-BiLSTM sequence exhibited the highest accuracy (96.91%) and sensitivity (96%) among the other thirteen machine learning models, hence, named as BivalveNet. Comparing the attained accuracy from the BivalveNet to other mollusc traceability studies, it was observed that an efficiency close to the attained using standard destructive, time-consuming, and expensive techniques, making BivalveNet a highly advantageous approach for common cockle geographical traceability studies, available for application to other bivalve species.

A surrogate non-intrusive reduced order model of quasi-geostrophic turbulence dynamics based on a combination of LSTM and different approaches of DMD

Mathematical modeling is applied to study phenomena and system behavior.In various engineering fields, many physical phenomena are illustrated using a set of differential equations.In many real-world applications, the mathematical models are very complex, and numerical simulations in high-dimensional systems are challenging.Examples of these problems are large-scale physical problems such as geophysical, which have high temporal and spatial variations.In these problems, model order reduction is a useful method for achieving an appropriate approximation because it can significantly decrease computational costs.Deep learning has recently been used to explore information from data and make predictions.There are several methods for dimensionality reduction.In this paper, we combine the dynamic mode decomposition (DMD) and the long short-term memory (LSTM) network.This is because LSTM can predict nonlinear systems and time series data.We use LSTM and DMD to predict nonlinear systems and reduce dimensions, respectively.Four common DMD schemes have been applied for dimensionality reduction.The common geophysical dataset has been used to evaluate the performance of the proposed method.Finally, we compare the variations of the modal coefficients which are obtained from snapshots projection and the reduced-order model.These results show the high accuracy of our proposed method.One of the things that is important is the time complexity of algorithm implementation.The time complexity of the proposed method is 10 times faster when 15 modes are used for modeling than when all features are used.