Deep neural network pruning algorithm based on particle swarm

The significant advantage of deep neural networks is that the upper layer can capture the high-level features of data based on the information acquired from the lower layer by stacking layers deeply. Since it is challenging to interpret what knowledge the neural network has learned, various studies for explaining neural networks have emerged to overcome this problem. However, these studies generate the local explanation of a single instance rather than providing a generalized global interpretation of the neural network model itself. To overcome such drawbacks of the previous approaches, we propose the global interpretation method for the deep neural network through features of the model. We first analyzed the relationship between the input and hidden layers to represent the high-level features of the model, then interpreted the decision-making process of neural networks through high-level features. In addition, we applied network pruning techniques to make concise explanations and analyzed the effect of layer complexity on interpretability. We present experiments on the proposed approach using three different datasets and show that our approach could generate global explanations on deep neural network models with high accuracy and fidelity.

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

Wireless Communications and Mobile Computing ◽

10.1155/2021/9933111 ◽

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.

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