Automated Hand-drawn sketches retrieval and recognition using regularized Particle Swarm Optimization based deep convolutional neural network

2020 ◽  
Author(s):  
M. Suresh Thangakrishnan ◽  
K. Ramar
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Convolutional Neural Network ◽  
Particle Swarm ◽  
Deep Convolutional Neural Network ◽  
Swarm Optimization

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.

Determination of stable structure of a cluster using convolutional neural network and particle swarm optimization

2021 ◽  
Vol 140 (3) ◽  
Author(s):  
Arka Mitra ◽  
Gourhari Jana ◽  
Ranita Pal ◽  
Pratiksha Gaikwad ◽  
Shamik Sural ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Convolutional Neural Network ◽  
Particle Swarm ◽  
Stable Structure ◽  
Swarm Optimization

scholarly journals Hyper-parameter optimization of convolutional neural network based on particle swarm optimization algorithm

2021 ◽  
Vol 10 (6) ◽  
pp. 3377-3384
Author(s):  
Zainab Fouad ◽  
Marco Alfonse ◽  
Mohamed Roushdy ◽  
Abdel-Badeeh M. Salem
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Convolutional Neural Network ◽  
Deep Neural Networks ◽  
Particle Swarm ◽  
Model Performance ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Testing Error

Deep neural networks have accomplished enormous progress in tackling many problems. More specifically, convolutional neural network (CNN) is a category of deep networks that have been a dominant technique in computer vision tasks. Despite that these deep neural networks are highly effective; the ideal structure is still an issue that needs a lot of investigation. Deep Convolutional Neural Network model is usually designed manually by trials and repeated tests which enormously constrain its application. Many hyper-parameters of the CNN can affect the model performance. These parameters are depth of the network, numbers of convolutional layers, and numbers of kernels with their sizes. Therefore, it may be a huge challenge to design an appropriate CNN model that uses optimized hyper-parameters and reduces the reliance on manual involvement and domain expertise. In this paper, a design architecture method for CNNs is proposed by utilization of particle swarm optimization (PSO) algorithm to learn the optimal CNN hyper-parameters values. In the experiment, we used Modified National Institute of Standards and Technology (MNIST) database of handwritten digit recognition. The experiments showed that our proposed approach can find an architecture that is competitive to the state-of-the-art models with a testing error of 0.87%.

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