IoT-based Epidemic Monitoring via Improved Gated Recurrent Unit Model

2021 ◽  
pp. 1-1
Author(s):  
Fang Hu ◽  
Jia Liu ◽  
Liuhuan Li ◽  
Mingfang Huang ◽  
Changguo Yang
Keyword(s):  
Epidemic Monitoring ◽  
Unit Model ◽  
Gated Recurrent Unit

scholarly journals Comparing word embedding models for Arabic aspect category detection using a deep learning-based approach

2021 ◽  
Vol 297 ◽  
pp. 01072
Author(s):  
Rajae Bensoltane ◽  
Taher Zaki
Keyword(s):  
Deep Learning ◽  
Vector Representation ◽  
Rule Based ◽  
External Resources ◽  
Unit Model ◽  
Proposed Model ◽  
Feature Based ◽  
The Impact ◽  
Gated Recurrent Unit ◽  
Machine Learning Models

Aspect category detection (ACD) is a task of aspect-based sentiment analysis (ABSA) that aims to identify the discussed category in a given review or sentence from a predefined list of categories. ABSA tasks were widely studied in English; however, studies in other low-resource languages such as Arabic are still limited. Moreover, most of the existing Arabic ABSA work is based on rule-based or feature-based machine learning models, which require a tedious task of feature-engineering and the use of external resources like lexicons. Therefore, the aim of this paper is to overcome these shortcomings by handling the ACD task using a deep learning method based on a bidirectional gated recurrent unit model. Additionally, we examine the impact of using different vector representation models on the performance of the proposed model. The experimental results show that our model outperforms the baseline and related work models significantly by achieving an enhanced F1-score of more than 7%.

scholarly journals A Hybrid Deep Neural Network for Electricity Theft Detection Using Intelligent Antenna-Based Smart Meters

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Ashraf Ullah ◽  
Nadeem Javaid ◽  
Adamu Sani Yahaya ◽  
Tanzeela Sultana ◽  
Fahad Ahmad Al-Zahrani ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Particle Swarm ◽  
Smart Meters ◽  
Swarm Optimization ◽  
Electricity Theft ◽  
Unit Model ◽  
Gated Recurrent Unit

This paper presents a hybrid model, named as hybrid deep neural network, which combines convolutional neural network, particle swarm optimization, and gated recurrent unit, termed as convolutional neural network-particle swarm optimization-gated recurrent unit model. The major aims of the model are to perform accurate electricity theft detection and to overcome the issues in the existing models. The issues include overfitting and inability of the models to handle imbalanced data. For this purpose, the electricity consumption data of smart meters is taken from state grid corporation of China. An electric utility company gathers the data from the intelligent antenna-based smart meters installed at the consumers’ end. The dataset contains real-time data with missing values and outliers. Therefore, it is first preprocessed to get the refined data followed by feature engineering for selection and extraction of the finest features from the dataset using convolutional neural network. The classification of electricity consumers is performed by dividing them into honest and fraudulent classes using the proposed particle swarm optimization-gated recurrent unit model. The proposed model is evaluated by performing simulations in terms of several performance measures that include accuracy, area under the curve, F 1 -score, recall, and precision. The comparison between the proposed hybrid deep neural network and benchmark models is also performed. The benchmark models include gated recurrent unit, long short term memory, logistic regression, support vector machine, and genetic algorithm-based gated recurrent unit. The results indicate that the proposed hybrid deep neural network model is more efficient in handling class imbalanced issues and performing electricity theft detection. The robustness, accuracy, and generalization of the model are also analyzed in the proposed work.

scholarly journals A Real-Time Trajectory Prediction Method of Small-Scale Quadrotors Based on GPS Data and Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7061
Author(s):  
Zhao Yang ◽  
Rong Tang ◽  
Jie Bao ◽  
Jiahuan Lu ◽  
Zhijie Zhang
Keyword(s):  
Real Time ◽  
Stable State ◽  
Prediction Method ◽  
Small Scale ◽  
Trajectory Data ◽  
Trajectory Prediction ◽  
Unit Model ◽  
Proposed Model ◽  
Historical Trajectory ◽  
Gated Recurrent Unit

This paper proposes a real-time trajectory prediction method for quadrotors based on a bidirectional gated recurrent unit model. Historical trajectory data of ten types of quadrotors were obtained. The bidirectional gated recurrent units were constructed and utilized to learn the historic data. The prediction results were compared with the traditional gated recurrent unit method to test its prediction performance. The efficiency of the proposed algorithm was investigated by comparing the training loss and training time. The results over the testing datasets showed that the proposed model produced better prediction results than the baseline models for all scenarios of the testing datasets. It was also found that the proposed model can converge to a stable state faster than the traditional gated recurrent unit model. Moreover, various types of training samples were applied and compared. With the same randomly selected test datasets, the performance of the prediction model can be improved by selecting the historical trajectory samples of the quadrotors close to the weight or volume of the target quadrotor for training. In addition, the performance of stable trajectory samples is significantly better than that with unstable trajectory segments with a frequent change of speed and direction with large angles.

scholarly journals A Parameter-Free Approach for Fault Section Detection on Distribution Networks Employing Gated Recurrent Unit

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6361
Author(s):  
Mohammad Reza Shadi ◽  
Hamid Mirshekali ◽  
Rahman Dashti ◽  
Mohammad-Taghi Ameli ◽  
Hamid Reza Shaker
Keyword(s):  
Distribution Networks ◽  
Economic Losses ◽  
Equipment Failure ◽  
Power Outages ◽  
Unit Model ◽  
Angle Data ◽  
Two Samples ◽  
Network Fault ◽  
Gated Recurrent Unit ◽  
Linear Nature

Faults in distribution networks can result in severe transients, equipment failure, and power outages. The quick and accurate detection of the faulty section enables the operator to avoid prolonged power outages and economic losses by quickly retrieving the network. However, the occurrence of diverse fault types with various resistances and locations and the highly non-linear nature of distribution networks make fault section detection challenging for numerous conventional techniques. This study presents a cutting-edge deep learning-based algorithm to distinguish fault sections in distribution networks to address these issues. The proposed gated recurrent unit model utilizes only two samples of the angle between the voltage and current on either side of the feeders, which record by smart feeder meters, to detect faulty sections in real time. When a network fault occurs, the protection relays trigger the trip command for the breakers. Immediately, the angle data are obtained from all smart feeder meters of the network, which comprises a pre-fault sample and a post-fault sample. The data are then employed as an input to the pre-trained gated recurrent unit model to determine the faulted line. The performance of this novel algorithm was validated through simulations of various fault types in the IEEE-33 bus system. The model recognizes the faulty section with competitive performance in terms of accuracy.

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