Short-term Memory Artificial Neural Network Research Articles

Automatic generation of learning outcomes based on long short–term memory artificial neural network1

Building a good instructional design requires a sound organization management to program and articulate several tasks based for instance on the time availability, process follow-up, social and educational context. Furthermore, learning outcomes are the basis involving every educational activity. Thus, based on a predefined ontology, including the instructional educative model and its characteristics, we propose the use of a Long Short–Term Memory Artificial Neural Network (LSTM) to organize the structure and automatize the obtention of learning outcomes for a focused instructional design. We present encouraging results in this direction through the use of a LSTM using as the training data, a small learning outcomes set predefined by the user, focused on the characteristics of an educative model previously defined.

Two-stage deep learning hybrid framework based on multi-factor multi-scale and intelligent optimization for air pollutant prediction and early warning.

Effective prediction of air pollution concentrations is of great importance to both the physical and mental health of citizens and urban pollution control. As one of the main components of air pollutants, accurate prediction of PM2.5 can provide a reference for air pollution control and pollution warning. This study proposes an air pollutant prediction and early warning framework, which innovatively combines feature extraction techniques, feature selection methods and intelligent optimization algorithms. First, the PM2.5 sequence is decomposed into several subsequences using the complete ensemble empirical mode decomposition with adaptive noise, and then the new components of the subsequences with different complexity are reconstructed using fuzzy entropy. Then, the Max-Relevance and Min-Redundancy method is used to select the influencing factors of the different reconstructed components. Then, a two-stage deep learning hybrid framework is constructed to model the prediction and nonlinear integration of the reconstructed components using a long short-term memory artificial neural network optimized by the gray wolf optimization algorithm. Finally, based on the proposed hybrid prediction framework, effective prediction and early warning of air pollutants are achieved. In an empirical study in three cities in China, the prediction accuracy, warning accuracy and prediction stability of the proposed hybrid framework outperformed the other comparative models. The analysis results indicate that the developed hybrid framework can be used as an effective tool for air pollutant prediction and early warning.

Long short-term memory artificial neural network approach to forecast meteorology and PM2.5 local variables in Bogotá, Colombia

Long short-term memory artificial neural network approach to forecast meteorology and PM2.5 local variables in Bogotá, Colombia

Application of Mathematical Model using Artificial Neural Network in Photovoltaic Electrical Power Generation Building

By counting the stock data of PV building integration-related companies in Shanghai and Shenzhen stock markets, a sector index model of PV building integration was established, moving averages were calculated and moving averages were plotted, and the prediction results of the model were used to compare with known data to optimize the model, and data from April 1, 2019 to May 6, 2021 were used as the training set and data from May 6, 2021 to May 28, 2021 were used as the test set by recurrent neural network and LSTM long short-term memory artificial neural network to optimize the model. as the training set and the data from May 6, 2021 to May 28, 2021 as the test set to train the optimized model to obtain the prediction results for the next month. Using the data from April 1, 2019 to May 28, 2021, a time period of 2 months is used to analyze the correlation between the SSE index and the PV building integration sector index for each period using Pearson correlation analysis. Finally, the time series forecasting model analysis is combined to assess the investment risk of individual stocks in the PV building integration sector.

Two brains, one target: Design of a multi-level information fusion model based on dual-subject RSVP

Background. Rapid serial visual presentation (RSVP) based brain-computer interface (BCI) is widely used to categorize the target and non-target images. The available information limits the prediction accuracy of single-trial using single-subject electroencephalography (EEG) signals.New Method. Hyperscanning is a new manner to record two or more subjects’ signals simultaneously. So we designed a multi-level information fusion model for target image detection based on dual-subject RSVP, namely HyperscanNet. The two modules of this model fuse the data and features of the two subjects at the data and feature layers. A chunked long and short-term memory artificial neural network (LSTM) was used in the time dimension to extract features at different periods separately, completing fine-grained underlying feature extraction. While the feature layer is fused, some plain operations are used to complete the fusion of the data layer to ensure that important information is not missed.Results. Experimental results show that the F1-score (the harmonic mean of precision and recall) of this method with best group of channels and segment length is 82.76%.Comparison with existing methods. This method improves the F1-score by at least 5% compared to single-subject target detection.Conclusions. Target detection can be accomplished by the two subjects’ collaboration to achieve a higher and more stable F1-score than a single subject.

Forecasting solar radiation using a deep long short-term memory artificial neural network

Solar systems are widely used to mitigate the environmental impact of the energy sector and their importance has constantly increased due to the recent EU’s strategy to lower the CO2 emissions. Moreover, the newest Energy of Buildings Directive empathises the importance of producing energy from renewable sources to decrease the overall impact of buildings over the total end-use energy consumption. Generally, the systems’ performances are highly correlated with the incident solar radiation and outdoor air temperature. Thus, being able to accurately forecast these two parameters represents a vital step in dimensioning and maximizing the overall energy production. This Paper presents the results obtained by implementing a deep recurrent artificial neural network (ANN) trained with on year solar radiation data harvested from the UPB campus. The time series data was modelled using a special ANN architecture – the LSTM (Long Short-Term Memory) – due to its special designed internal ‘memory’ which increases its capabilities of predicting temporal sequence data. The model uses sequences of 24 hours and the resulted mean squared error (mse) for both training and validation data is under 30%.

Analysis of Lumber Prices Time Series Using Long Short-Term Memory Artificial Neural Networks

This manuscript confirms the feasibility of using a long short-term memory (LSTM) recurrent neural network (RNN) to forecast lumber stock prices during the great and Coronavirus disease 2019 (COVID-19) pandemic recessions in the USA. The database was composed of 5012 data entries divided into recession periods. We applied a timeseries cross-validation that divided the dataset into an 80:20 training/validation ratio. The network contained five LSTM layers with 50 units each followed by a dense output layer. We evaluated the performance of the network via mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) for 30, 60, and 120 timesteps and the recession periods. The metrics results indicated that the network was able to capture the trend for both recession periods with a remarkably low degree of error. Timeseries forecasting may help the forest and forest product industries to manage their inventory, transportation costs, and response readiness to critical economic events.

Learning chemistry: exploring the suitability of machine learning for the task of structure-based chemical ontology classification

Chemical data is increasingly openly available in databases such as PubChem, which contains approximately 110 million compound entries as of February 2021. With the availability of data at such scale, the burden has shifted to organisation, analysis and interpretation. Chemical ontologies provide structured classifications of chemical entities that can be used for navigation and filtering of the large chemical space. ChEBI is a prominent example of a chemical ontology, widely used in life science contexts. However, ChEBI is manually maintained and as such cannot easily scale to the full scope of public chemical data. There is a need for tools that are able to automatically classify chemical data into chemical ontologies, which can be framed as a hierarchical multi-class classification problem. In this paper we evaluate machine learning approaches for this task, comparing different learning frameworks including logistic regression, decision trees and long short-term memory artificial neural networks, and different encoding approaches for the chemical structures, including cheminformatics fingerprints and character-based encoding from chemical line notation representations. We find that classical learning approaches such as logistic regression perform well with sets of relatively specific, disjoint chemical classes, while the neural network is able to handle larger sets of overlapping classes but needs more examples per class to learn from, and is not able to make a class prediction for every molecule. Future work will explore hybrid and ensemble approaches, as well as alternative network architectures including neuro-symbolic approaches.

Economic Nowcasting with Long Short-term Memory Artificial Neural Networks (LSTM)

Artificial neural networks (ANNs) have been the catalyst to numerous advances in a variety of fields and disciplines in recent years. Their impact on economics, however, has been comparatively muted. One type of ANN, the long short-term memory network (LSTM), is particularly well-suited to deal with economic time-series. Here, the architecture’s performance and characteristics are evaluated in comparison with the dynamic factor model (DFM), currently a popular choice in the field of economic nowcasting. LSTMs are found to produce superior results to DFMs in the nowcasting of three separate variables; global merchandise export values and volumes, and global services exports. Further advantages include their ability to handle large numbers of input features in a variety of time frequencies. A disadvantage is the inability to ascribe contributions of input features to model outputs, common to all ANNs. In order to facilitate continued applied research of the methodology by avoiding the need for any knowledge of deep-learning libraries, an accompanying Python library was developed using PyTorch: https://pypi.org/project/nowcast-lstm/.

External Validation of the Long Short-Term Memory Artificial Neural Network-Based SCaP Survival Calculator for Prediction of Prostate Cancer Survival.

PurposeDecision-making for treatment of newly diagnosed prostate cancer (PCa) is complex due to the multiple initial treatment modalities available. We aimed to externally validate the SCaP (Severance Study Group of Prostate Cancer) Survival Calculator that incorporates a long short-term memory artificial neural network (ANN) model to estimate survival outcomes of PCa according to initial treatment modality.Materials and MethodsThe validation cohort consisted of clinicopathological data of 4,415 patients diagnosed with biopsy-proven PCa between April 2005 and November 2018 at three institutions. Area under the curves (AUCs) and time-to-event calibration plots were utilized to determine the predictive accuracies of the SCaP Survival Calculator in terms of progression to castration-resistant PCa (CRPC)–free survival, cancer-specific survival (CSS), and overall survival (OS).ResultsExcellent discrimination was observed for CRPC-free survival, CSS, and OS outcomes, with AUCs of 0.962, 0.944, and 0.884 for 5-year outcomes and 0.959, 0.928, and 0.854 for 10-year outcomes, respectively. The AUC values were higher for all survival endpoints compared to those of the development cohort. Calibration plots showed that predicted probabilities of 5-year survival endpoints had concordance comparable to those of the observed frequencies. However, calibration performances declined for 10-year predictions with an overall underestimation.ConclusionThe SCaP Survival Calculator is a reliable and useful tool for determining the optimal initial treatment modality and for guiding survival predictions for patients with newly diagnosed PCa. Further modifications in the ANN model incorporating cases with more extended follow-up periods are warranted to improve the ANN model for long-term predictions.

Utilization of a reinforcement learning algorithm for the accurate alignment of a robotic arm in a complete soft fabric shoe tongues automation process

As usher in Industry 4.0, there has been much interest in the development and research that combine artificial intelligence with automation. The control and operation of equipment in a traditional automated shoemaking production line require a preliminary subjective judgment of relevant manufacturing processes, to determine the exact procedure and corresponding control settings. However, with the manual control setting, it is difficult to achieve an accurate quality assessment of an automated process characterized by high uncertainty and intricacy. It is challenging to replace handicrafts and the versatility of manual product customization with automation techniques. Hence, the current study has developed an automatic production line with a cyber-physical system artificial intelligence (CPS-AI) architecture for the complete manufacturing of soft fabric shoe tongues. The Deep-Q reinforcement learning (RL) method is proposed as a means of achieving better control over the manufacturing process, while the convolutional and long short-term memory artificial neural network (CNN + LSTM) is developed to enhance action speed. This technology allows a robotic arm to learn the specific image feature points of a shoe tongue through repeated training to improve its manufacturing accuracy. For validation, different parameters of the network architecture are tested, and the test convergence accuracy was found to be as high as 95.9 %. During its actual implementation, the production line completed 509 finished products, of which 349 products were acceptable due to the anticipated measurement error. This showed that the production line system was capable of achieving optimum product accuracy and quality with respect to the performance of repeated computations, parameter updates, and action evaluations.

A long short-term memory artificial neural network to predict daily HVAC consumption in buildings

In this paper, the design and implementation process of an artificial neural network based predictor to forecast a day ahead of the power consumption of a building HVAC system is presented. The featured HVAC system is situated at MagicBox, a real self-sufficient solar house with a monitoring system. Day ahead prediction of HVAC power consumption will remarkably enhance the Demand Side Management techniques based on appliance scheduling to reach defined goals. Several multi step prediction models, based on LSTM neural networks, are proposed. In addition, suitable data preprocessing and arrangement techniques are set to adapt the raw dataset. Considering the targeted prediction horizon, the models provide outstanding results in terms of test errors (NRMSE of 0.13) and correlation, between the temporal behavior of the predictions and test time series to be forecasted, of 0.797. Moreover, these results are compared to the simplified one hour ahead prediction that reaches nearly optimal test NRMSE of 0.052 and Pearson correlation coefficient of 0.972. These results provide an encouraging perspective for real-time energy consumption prediction in buildings.

Long short-term memory artificial neural network model for prediction of prostate cancer survival outcomes according to initial treatment strategy: development of an online decision-making support system.

The delivery of precision medicine is a primary objective for both clinical and translational investigators. Patients with newly diagnosed prostate cancer (PCa) face the challenge of deciding among multiple initial treatment modalities. The purpose of this study is to utilize artificial neural network (ANN) modeling to predict survival outcomes according to initial treatment modality and to develop an online decision-making support system. Data were collected retrospectively from 7267 patients diagnosed with PCa between January 1988 and December 2017. The analyses included 19 pretreatment clinicopathological covariates. Multilayer perceptron (MLP), MLP for N-year survival prediction (MLP-N), and long short-term memory (LSTM) ANN models were used to analyze progression to castration-resistant PCa (CRPC)-free survival, cancer-specific survival (CSS), and overall survival (OS), according to initial treatment modality. The performances of the ANN and the Cox-proportional hazards regression models were compared using Harrell's C-index. The ANN models provided higher predictive power for 5- and 10-year progression to CRPC-free survival, CSS, and OS compared to the Cox-proportional hazards regression model. The LSTM model achieved the highest predictive power, followed by the MLP-N, and MLP models. We developed an online decision-making support system based on the LSTM model to provide individualized survival outcomes at 5 and 10years, according to the initial treatment strategy. The LSTM ANN model may provide individualized survival outcomes of PCa according to initial treatment strategy. Our online decision-making support system can be utilized by patients and health-care providers to determine the optimal initial treatment modality and to guide survival predictions.