scholarly journals A Deep Recurrent Neural Network for Non-Intrusive Load Monitoring Based on Multi-Feature Input Space and Post-Processing

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2195
Author(s):  
Hasan Rafiq ◽  
Xiaohan Shi ◽  
Hengxu Zhang ◽  
Huimin Li ◽  
Manesh Kumar Ochani
Keyword(s):  
Neural Network ◽  
Power Consumption ◽  
Real Time ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Estimation Accuracy ◽  
Post Processing ◽  
Input Space ◽  
Deep Recurrent Neural Network ◽  
Load Monitoring

Non-intrusive load monitoring (NILM) is a process of estimating operational states and power consumption of individual appliances, which if implemented in real-time, can provide actionable feedback in terms of energy usage and personalized recommendations to consumers. Intelligent disaggregation algorithms such as deep neural networks can fulfill this objective if they possess high estimation accuracy and lowest generalization error. In order to achieve these two goals, this paper presents a disaggregation algorithm based on a deep recurrent neural network using multi-feature input space and post-processing. First, the mutual information method was used to select electrical parameters that had the most influence on the power consumption of each target appliance. Second, selected steady-state parameters based multi-feature input space (MFS) was used to train the 4-layered bidirectional long short-term memory (LSTM) model for each target appliance. Finally, a post-processing technique was used at the disaggregation stage to eliminate irrelevant predicted sequences, enhancing the classification and estimation accuracy of the algorithm. A comprehensive evaluation was conducted on 1-Hz sampled UKDALE and ECO datasets in a noised scenario with seen and unseen test cases. Performance evaluation showed that the MFS-LSTM algorithm is computationally efficient, scalable, and possesses better estimation accuracy in a noised scenario, and generalized to unseen loads as compared to benchmark algorithms. Presented results proved that the proposed algorithm fulfills practical application requirements and can be deployed in real-time.

A Robust False Spam Review Detection Using Deep Long Short-Term Memory (LSTM) Based Recurrent Neural Network

2020 ◽  
Vol 17 (8) ◽  
pp. 3421-3426
Author(s):  
D. Deva Hema ◽  
J. Tharun ◽  
G. Arun Dev ◽  
N. Sateesh
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Online Reviews ◽  
Physical Contact ◽  
Proposed Model ◽  
Deep Recurrent Neural Network ◽  
Bayes Algorithm ◽  
Fake Reviews

Our day-to-day activity is highly influenced by development of Internet. One of the rapid growing area in Internet is E-commerce. People are eager to buy products from online sites like Amazon, embay, Flipkart etc. Customers can write reviews about the products purchased online. The purchasing of good through online has been increasing exponentially since last few years. As there is no physical contact with goods before purchasing through online, people totally rely on reviews about the product before purchasing it. Hence review plays an important role in deciding the quality of the product. There are many customers who give online reviews about the product after using it. Hence the quality of the product is decided by the reviews of the customers. Thus, detection of fake reviews has become one of the important task. The proposed system will help in finding such fake reviews about the product, so that the fake reviews can be eliminated. Therefore, the purchasing of the products will be totally based on the genuine reviews. The proposed system uses Deep Recurrent Neural Network (DRNN) to predict the fake reviews and the performance of the proposed method has compared with Naïve Bayes Algorithm. The proposed model shows good accuracy and can handle huge amount of data over the existing system.

scholarly journals Latent representation of the human pan-celltype epigenome through a deep recurrent neural network

2021 ◽  
Author(s):  
Kevin B. Dsouza ◽  
Adam Y. Li ◽  
Vijay K. Bhargava ◽  
Maxwell W. Libbrecht
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Replication Timing ◽  
Cell Types ◽  
Data Sets ◽  
Deep Recurrent Neural Network ◽  
Long Short Term Memory ◽  
Latent Representations

AbstractThe availability of thousands of assays of epigenetic activity necessitates compressed representations of these data sets that summarize the epigenetic landscape of the genome. Until recently, most such representations were celltype specific, applying to a single tissue or cell state. Recently, neural networks have made it possible to summarize data across tissues to produce a pan-celltype representation. In this work, we propose Epi-LSTM, a deep long short-term memory (LSTM) recurrent neural network autoencoder to capture the long-term dependencies in the epigenomic data. The latent representations from Epi-LSTM capture a variety of genomic phenomena, including gene-expression, promoter-enhancer interactions, replication timing, frequently interacting regions and evolutionary conservation. These representations outperform existing methods in a majority of cell-types, while yielding smoother representations along the genomic axis due to their sequential nature.

scholarly journals Stacked LSTM based deep recurrent neural network with kalman smoothing for blood glucose prediction

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Md Fazle Rabby ◽  
Yazhou Tu ◽  
Md Imran Hossen ◽  
Insup Lee ◽  
Anthony S. Maida ◽  
...  
Keyword(s):  
Neural Network ◽  
Blood Glucose ◽  
Recurrent Neural Network ◽  
Insulin Infusion ◽  
Diabetes Management ◽  
Short Term Memory ◽  
Artificial Pancreas ◽  
Time Interval ◽  
Kalman Smoothing ◽  
Deep Recurrent Neural Network

Abstract Background Blood glucose (BG) management is crucial for type-1 diabetes patients resulting in the necessity of reliable artificial pancreas or insulin infusion systems. In recent years, deep learning techniques have been utilized for a more accurate BG level prediction system. However, continuous glucose monitoring (CGM) readings are susceptible to sensor errors. As a result, inaccurate CGM readings would affect BG prediction and make it unreliable, even if the most optimal machine learning model is used. Methods In this work, we propose a novel approach to predicting blood glucose level with a stacked Long short-term memory (LSTM) based deep recurrent neural network (RNN) model considering sensor fault. We use the Kalman smoothing technique for the correction of the inaccurate CGM readings due to sensor error. Results For the OhioT1DM (2018) dataset, containing eight weeks’ data from six different patients, we achieve an average RMSE of 6.45 and 17.24 mg/dl for 30 min and 60 min of prediction horizon (PH), respectively. Conclusions To the best of our knowledge, this is the leading average prediction accuracy for the ohioT1DM dataset. Different physiological information, e.g., Kalman smoothed CGM data, carbohydrates from the meal, bolus insulin, and cumulative step counts in a fixed time interval, are crafted to represent meaningful features used as input to the model. The goal of our approach is to lower the difference between the predicted CGM values and the fingerstick blood glucose readings—the ground truth. Our results indicate that the proposed approach is feasible for more reliable BG forecasting that might improve the performance of the artificial pancreas and insulin infusion system for T1D diabetes management.

Maritime Anomaly Detection using Density-based Clustering and Recurrent Neural Network

2019 ◽  
Vol 72 (04) ◽  
pp. 894-916 ◽  
Author(s):  
Liangbin Zhao ◽  
Guoyou Shi
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Real Time ◽  
Recurrent Neural Network ◽  
Situational Awareness ◽  
Short Term Memory ◽  
Spatial Clustering ◽  
Dbscan Algorithm ◽  
The Neural Network ◽  
Density Based Clustering

Maritime anomaly detection can improve the situational awareness of vessel traffic supervisors and reduce maritime accidents. In order to better detect anomalous behaviour of a vessel in real time, a method that consists of a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and a recurrent neural network is presented. In the method presented, the parameters of the DBSCAN algorithm were determined through statistical analysis, and the results of clustering were taken as the traffic patterns to train a recurrent neural network composed of Long Short-Term Memory (LSTM) units. The neural network was applied as a vessel trajectory predictor to conduct real-time maritime anomaly detection. Based on data from the Chinese Zhoushan Islands, experiments verified the applicability of the proposed method. The results show that the proposed method can detect anomalous behaviours of a vessel regarding speed, course and route quickly.

scholarly journals Power System Load Forecasting Method Based on Recurrent Neural Network

2020 ◽  
Vol 182 ◽  
pp. 02007
Author(s):  
Chuanjun Pang ◽  
Tie Bao ◽  
Lei He
Keyword(s):  
Neural Network ◽  
Power System ◽  
Recurrent Neural Network ◽  
Electricity Market ◽  
Short Term Memory ◽  
Sequence Data ◽  
Load Forecasting ◽  
System Load ◽  
Forecasting Method ◽  
Deep Recurrent Neural Network

Power system load forecasting plays an important role in the power dispatching operation. The development of the electricity market and the increasing integration of distributed generators have increased the complexity of power consumption model and put forward higher requirements for the accuracy and stability of load forecasting. A load forecasting method based on long-short term memory (LSTM) is proposed. This method uses deep recurrent neural network from the artificial intelligence field to establish a load forecasting model. Using the LSTM network to memorize the long-term dependence of the sequence data, the intrinsic variation of the load itself is identified from both the horizontal and vertical dimensions within a longer historical time period, while considering various influencing factors. Actual load data is used to verify the forecasting performance of different historical date windows and different network architectures.

scholarly journals ECG Identification For Personal Authentication Using LSTM-Based Deep Recurrent Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3069 ◽  
Author(s):  
Beom-Hun Kim ◽  
Jae-Young Pyun
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Recurrent Neural Network ◽  
Recurrent Neural Networks ◽  
Support Vector ◽  
Time System ◽  
Real Time System ◽  
Deep Recurrent Neural Network ◽  
Personal Authentication

Securing personal authentication is an important study in the field of security. Particularly, fingerprinting and face recognition have been used for personal authentication. However, these systems suffer from certain issues, such as fingerprinting forgery, or environmental obstacles. To address forgery or spoofing identification problems, various approaches have been considered, including electrocardiogram (ECG). For ECG identification, linear discriminant analysis (LDA), support vector machine (SVM), principal component analysis (PCA), deep recurrent neural network (DRNN), and recurrent neural network (RNN) have been conventionally used. Certain studies have shown that the RNN model yields the best performance in ECG identification as compared with the other models. However, these methods require a lengthy input signal for high accuracy. Thus, these methods may not be applied to a real-time system. In this study, we propose using bidirectional long short-term memory (LSTM)-based deep recurrent neural networks (DRNN) through late-fusion to develop a real-time system for ECG-based biometrics identification and classification. We suggest a preprocessing procedure for the quick identification and noise reduction, such as a derivative filter, moving average filter, and normalization. We experimentally evaluated the proposed method using two public datasets: MIT-BIH Normal Sinus Rhythm (NSRDB) and MIT-BIH Arrhythmia (MITDB). The proposed LSTM-based DRNN model shows that in NSRDB, the overall precision was 100%, recall was 100%, accuracy was 100%, and F1-score was 1. For MITDB, the overall precision was 99.8%, recall was 99.8%, accuracy was 99.8%, and F1-score was 0.99. Our experiments demonstrate that the proposed model achieves an overall higher classification accuracy and efficiency compared with the conventional LSTM approach.

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