Deep LSTM Research Articles

LSTM Deep Neural Network Based Power Data Credit Tagging Technology

LSTM Deep Neural Network Based Power Data Credit Tagging Technology

Extraction of Technical Indicators and Data Augmentation-Based Stock Market Prediction Using Deep LSTM Integrated Competitive Swarm Feedback Algorithm

In recent decades, time-series analysis and value forecasting have grown in research fields. Making a significant decision in the stock market prediction strategy requires knowledge that may be gained by forecasting with time-series data. Various prediction methods are developed to forecast future stock prices, but accurate prediction with the time-series data using external factors still results in a difficult task. An effective prediction approach is designed in this paper using the adopted Competitive Swarm Feedback Algorithm-based Deep Long Short-Term Memory (CSFA-based Deep LSTM) classifier for predicting the stock market data. The CSFA is the integration of the Competitive Swarm Optimizer (CSO) and Feedback Artificial Tree (FAT) algorithm. Then, technical indicators’ extraction, feature fusion and data augmentation steps are carried out. The Deep LSTM achieved higher prediction results than the other traditional classifiers. The proposed method achieved lower MAE, lower MSE, and minimum RMSE with the values of 0.1418, 0.1119, and 0.2557.

An optimized automated recognition of infant sign language using enhanced convolution neural network and deep LSTM

An optimized automated recognition of infant sign language using enhanced convolution neural network and deep LSTM

Deep learning for crown profile modelling of Pinus yunnanensis secondary forests in Southwest China.

Accurate information concerning crown profile is critical in analyzing biological processes and providing a more accurate estimate of carbon balance, which is conducive to sustainable forest management and planning. The similarities between the types of data addressed with LSTM algorithms and crown profile data make a compelling argument for the integration of deep learning into the crown profile modeling. Thus, the aim was to study the application of deep learning method LSTM and its variant algorithms in the crown profile modeling, using the crown profile database from Pinus yunnanensis secondary forests in Yunnan province, in southwest China. Furthermore, the SHAP (SHapley Additive exPlanations) was used to interpret the predictions of ensemble or deep learning models. The results showed that LSTM's variant algorithms was competitive with traditional Vanila LSTM, but substantially outperformed ensemble learning model LightGBM. Specifically, the proposed Hybrid LSTM-LightGBM and Integrated LSTM-LightGBM have achieved a best forecasting performance on training set and testing set respectively. Furthermore, the feature importance analysis of LightGBM and Vanila LSTM presented that there were more factors that contribute significantly to Vanila LSTM model compared to LightGBM model. This phenomenon can explain why deep learning outperforms ensemble learning when there are more interrelated features.

Hybrid optimization enabled deep learning and spark architecture using big data analytics for stock market forecasting

SummaryThe precise forecasting of stock prices is not possible because of the complexity and uncertainty of stock. The effectual model is needed for the triumphant assessment of upcoming stock prices for several companies. Here, an optimized deep model is utilized to effectively predict the stock market using the spark framework. Here, the data partitioning is done using deep embedded clustering, wherein the tuning of parameters is done using the proposed Jaya Anti Coronavirus Optimization (JACO) algorithm in the master node. The proposed JACO is developed by combining Jaya Algorithm and Anti‐Coronavirus Optimization algorithm. Then, important technical indicators are mined from divided data in slave nodes. Here, the technical indicators are considered features for enhanced processing. Then, data augmentation is done to make data suitable for processing in the master node. At last, the prediction was done in the master node using deep long short‐term memory (Deep LSTM), and training is performed with the proposed JACO. The proposed JACO‐based Deep LSTM attains the smallest mean absolute error of 0.113, mean squared error of 0.095, and root mean squared error of 0.309.

An effective recognition of moving target seismic anomaly for security region based on deep bidirectional LSTM combined CNN

An effective recognition of moving target seismic anomaly for security region based on deep bidirectional LSTM combined CNN

Human Action Recognition of Autonomous Mobile Robot Using Edge-AI

The development of autonomous mobile robots (AMRs) has brought with its requirements for intelligence and safety. Human action recognition (HAR) within AMR has become increasingly important because it provides interactive cognition between human and AMR. This study presents a full architecture for edge-artificial intelligence HAR (Edge-AI HAR) to allow AMR to detect human actions in real time. The architecture consists of three parts: a human detection and tracking network, a key frame extraction function, and a HAR network. The HAR network is a cascade of a DenseNet121 and a double-layer bidirectional long-short-term-memory (DLBiLSTM), in which the DenseNet121 is a pretrained model to extract spatial features from action key frames and the DLBiLSTM provides a deep two-directional LSTM inference to classify complicated time-series human actions. Edge-AI HAR undergoes two optimizations—ROS distributed computation and TensorRT structure optimization—to give a small model structure and high computational efficiency. Edge-AI HAR is demonstrated in two experiments using an AMR and is demonstrated to give an average precision of 97.58% for single action recognition and around 86% for continuous action recognition.

Spectrum Sensing in Cognitive Radio for Internet of Things using Deep Learning Models

An intelligent wireless communication system capable of learning from its surroundings is called cognitive radio. It permits Secondary Users to reuse the radio resources that are made available to them while avoiding damaging interference with licensed users. The key component of cognitive radio technology is spectrum sensing. The literature has become interested in the use of machine learning approaches for spectrum sensing. Enhancement of performance through further spectrum availability forecast and comprehension of the first activities of the primary user. Supporting many SUs at once will boost the speed of spectrum sensing and data transfer over the available, limited spectrum. The performance of current spectrum sensing techniques is constrained by severe channel conditions.With the advent of deep learning in various fields, we are trying to analyze the performance of spectrum sensing (SS) wherein deep learning method is adopted for data fusion. In this research, a data-driven deep learning model is suggested to automatically classify the received raw signal data, which is regarded as time-series data. In this paper, we have provided a comparative analysis of various deep learning models, including ResNet, VGG, LSTM, and MLP. The performance comparison was provided using various sample lengths and SNR values, both low and high. The ResNet model’s performance has produced the highest detection probabilities in both low and high SNR ratios. The simulation result of the proposed methodology would improve the system’s accuracy with a reduction in losses that occurred during the false alarm of prediction as well as an improvement in the probability of detection. Therefore, simulation results of the suggested methodology would lead to an improvement in the system’s accuracy, a decrease in losses from false alarms of prediction, as well as an increase in the likelihood of detection. Better spectrum sensing would be achieved through deep learning’s analysis of PU statistics

Multi-level Signal Decomposition for Power Quality Disturbance Classification

The introduction of electric vehicles impose large disturbance to the grid-level power signal due to the charging and discharging mechanism. Power signal monitoring in the electrical grid can provide several insights such as power quality disturbance detection, major power consumption area, peak power usage period, and their potential catastrophic failure conditions. As for preventive maintenance purpose, automatic classification of power quality disturbance using a hybrid method incorporating wavelet transform and deep LSTM network is proposed in this paper. Multi-level signal decomposition is applied to input signal to increase the resolution of input decomposing into multiple frequency bands. Subsequently, these multi-level frequency components are fed into deep LSTM layer to further extract useful higher order latent feature. Classification performance of the proposed wavelet-based LSTM (WTLSTM) network is bench-marked with deep LSTM method. Additive white Gaussian noise (AWGN) with signal-to-noise (SNR) levels between 20-50dB are inserted during the training process to increase the generalization of signal learning with the realistic scenarios. The classification performance of both WT-LSTM and Deep LSTM networks are tested with 20,30,40,50dB SNR AWGN and noiseless conditions. As a result, the WT-LSTM network obtains an overall classification performance of 89.77% on 20dB and 99.21% on noiseless condition as compared to Deep LSTM, with 88.48% and 98.54% respectively.

Multivariate Time Series Sensor Feature Forecasting Using Deep Bidirectional LSTM

Multivariate Time series data forecasting (MTSF) is the assignment of forecasting future estimates of a particular series employing historic data. Lately, this work has enticed the focus of machine and deep learning researchers to tackle the complex and time consuming aspects of conventional forecasting techniques. Along with increasing access to historic documented data and the dire need of carrying out precise time-series feature forecasting. In this paper, we put forward a deep-learning(DL) technique proficient to tackle the setback of conventional forecasting techniques and display precise forecasting. The proposed technique is a Stacked Bidirectional long-short term memory architecture, which is an improvised version of existing Bidirectional LSTM in which multiple Bidirectional LSTM blocks are stacked, such that each layer contains multiple cells. In the direction of fair assessment, the functioning of the proposed technique is compared with existing LSTMs. Using varied evaluation measures, the obtained results show that the proposed Deep Bidirectional long-short term memory model exceeds standard approaches.

Design and Development of a Short-Term Photovoltaic Power Output Forecasting Method Based on Random Forest, Deep Neural Network and LSTM Using Readily Available Weather Features

Renewable energy sources (RES) are an essential part of building a more sustainable future, with higher diversity of clean energy, reduced emissions and less dependence on finite fossil fuels such as coal, oil and natural gas. The advancements in the renewable energy sources domain bring higher hardware efficiency and lower costs, which improves the likelihood of wider RES adoption. However, integrating renewables such as photovoltaic (PV) systems in the current grid is still a major challenge. The main reason is the volatile, intermittent nature of RES, which increases the complexity of the grid management and maintenance. Having access to accurate PV power output forecasting could reduce the number of power supply disruptions, improve the planning of the available and reserve capacities and decrease the management and operational costs. In this context, this paper explores and evaluates three Artificial Intelligence (AI) methods - random forest (RF), deep neural network (DNN) and long short-term memory network (LSTM), which are applied for the task of short-term PV output power forecasting. Following a statistical forecasting approach, the selected models are trained on weather and PV output data collected in Berlin, Germany. The assembled data set contains predominantly broadly accessible weather features, which makes the proposed approach more cost efficient and easily applicable even for geographic locations without access to specialized hardware or hard-to-obtain input features. The performance achieved by two of the selected algorithms indicates that the RF and the DNN models are able to generate accurate solar power forecasts and are also able to handle sudden changes and shifts in the PV power output.

Deep belief bi-directional LSTM network-based intelligent student's performance prediction model with entropy weighted fuzzy rough set mining

Deep belief bi-directional LSTM network-based intelligent student's performance prediction model with entropy weighted fuzzy rough set mining

Deep belief bi-directional LSTM network-based intelligent students performance prediction model with entropy weighted fuzzy rough set mining

Deep belief bi-directional LSTM network-based intelligent students performance prediction model with entropy weighted fuzzy rough set mining

Retracted: Classification of Alzheimer's Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network.

[This retracts the article DOI: 10.1155/2021/4186666.].

Enhanced security using video summarization for surveillance system using deep LSTM model with K-means clustering technique

For the security of the Society, Video surveillance systems have become an important tool that can help in preventing crime in public or private establishments, leading to more safe and secure environment. With the rise of technology that can handle high volune video data and the need for continuous monitoring for security to citizens, the analysis of the video footages, during security breach attempts has become more challenging. Video summarization can play a crucial role in secure surveillance systems by optimizing the analysis time of the security breach. This research attempts to summarise the important aspects of the original video without losing its context and also optimizes the storage requirement. To achieve the same a unique KM-LSTM based technique has been developed that utilizes deep learning methods to understand links among highlight as well as non-highlight video segments through the use of pairwise distance calculations. A new Deep LSTM model has been used to extract the features from all segments of frames. These extracted features are clustered using k-means clustering. Then, such features are trained using a pairwise deep learning model to get highlight scores as well as leverage the comparative similarity among pairs of the highlight as well as non-highlight segments. At last, video summarization takes place to generate the video summary from the highlight segments only. From the results of these experiments discovering the moments of the user’s major or special interest (namely, highlights) in a video, to generating summarization of videos. The above technique can help in increasing the analysis of Video content and thus achieving a more vigilant and secure society.

Deep LSTM model exploiting optical sensors for soil nutrient prediction

Deep LSTM model exploiting optical sensors for soil nutrient prediction

Forest Change Detection and Prediction using the Crow Chicken Swarm Optimization based Deep LSTM Classifier

Forest Change Detection and Prediction using the Crow Chicken Swarm Optimization based Deep LSTM Classifier

Application of Deep Learning LSTM in Online Power Prediction on Three-Phase Power Transformer

Electrical energy plays an important role in daily life, especially companies. The 3 Phase Power Transformer is one of the important electrical components that is very influential in distributing electrical energy to companies as is the case in PT. Semen Baturaja (Persero). 3-phase power transformers require attention because they are one of the components that are prone to interference, this interference can hinder the effectiveness of using electrical energy as a company support for employees to work. As one of the disturbances for 3-phase power transformers is overload or excessive power usage, overload can raise the temperature at the winding and reduce its service life. Artificial intelligence can be one of the keys to predict the use of power transformers in the future, especially deep learning by utilizing the LSTM algorithm. Optimal power prediction requires a lot of maximum input variables so that in this study, it not only adds an offline learning mode but adds a learning mode that can directly access the company's Power Quality Monitoring (PQM) website online with an average accuracy value of 86.62%.

Application of Deep Learning LSTM and ARIMA Models in Time Series Forecasting: A Methods Case Study analyzing Canadian and Swedish Indoor Air Pollution Data

Application of Deep Learning LSTM and ARIMA Models in Time Series Forecasting: A Methods Case Study analyzing Canadian and Swedish Indoor Air Pollution Data

Stock Price Prediction using a Deep Learning Algorithm LSTM and Machine Learning

Stock Price Prediction using a Deep Learning Algorithm LSTM and Machine Learning