Hybrid deep neural network with adaptive galactic swarm optimization for text extraction from scene images

In this article, a novel performance-seeking control method based on deep neural network and interval analysis is proposed to obtain a better engine performance. A deep neural network modeling method which has stronger representation capability than conventional neural network and can deal with big training data is adopted to establish an on-board model in the subsonic and supersonic cruising envelops. Meanwhile, a global optimization algorithm interval analysis is applied here to get a better engine performance. Finally, two simulation experiments are conducted to verify the effectiveness of the proposed methods. One is the on-board model modeling which compares the deep neural network with the conventional neural network, and the other is the performance-seeking control simulations comparing interval analysis with feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm, respectively. These two experiments show that the deep neural network has much higher precision than the conventional neural network and the interval analysis gets much better engine performance than feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm.

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.

This work reports a novel method by fusing Laplacian Eigenmaps feature conversion and deep neural network (DNN) for machine condition assessment. Laplacian Eigenmaps is adopted to transform data features from original high dimension space to projected lower dimensional space, the DNN is optimized by the particle swarm optimization algorithm, and the machine run-to-failure experiment were investigated for validation studies. Through a series of comparative experiments with the original features, two other effective space transformation techniques, Principal Component Analysis (PCA) and Isometric map (Isomap), and two other artificial intelligence methods, hidden Markov model (HMM) as well as back-propagation neural network (BPNN), the present method in this paper proved to be more effective for machine operation condition assessment.

The published article [1] has been retracted at the request of the authors [...]