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This paper proposes a novel and robust predictive method using modified spider monkey optimization (MSMO) and probabilistic neural network (PNN) for face recognition. The limitation of the traditional spider monkey optimization (SMO) approach to obtaining an optimal solution for classification problems is overcome by enhancing the performance of SMO by modifying the perturbation rate with a non‐linear function, thereby improving the convergence of SMO. The framework comprises image preprocessing, feature extraction using dual tree complex wavelet transform (DT‐CWT), feature selection using the modified spider monkey optimization algorithm (MSMO), and classification using PNN. The proposed method is tested on the Yale and AR Face datasets. Experimental outcomes reveal that the proposed framework attain an accuracy of 99.4% with appreciable sensitivity, specificity, and G‐mean. To examine the efficacy of MSMO, parametric studies are conducted, which showed that MSMO converges faster with high fitness when compared to similar evolutionary algorithms like Genetic Algorithm (GA), Grey Wolf Optimization Algorithm (GWO), Particle Swarm Algorithm (PSO), and Cuckoo Search (CS) in selecting the optimal feature set. The MSMO‐PNN method outperforms similar state‐of‐the‐art methods, which reveals that the method proposed is competitive. The proposed model is robust to Gaussian and salt–pepper noise, obtaining the highest accuracy of 97.89% for varied noise density and variance. [ABSTRACT FROM AUTHOR]

In this article, a deep neural network is proposed that can be used to perform a quantized compressed sensing of remote sensing images. This network, dubbed RQCSNet consists of three parts: sensing, nonlinear quantization, and reconstruction. All network's parts are trained simultaneously according to the input data. RQCSNet has fully convolutional architecture, and it can work with different resolutions of the input images without the need for large initial fully connected layers. Furthermore, its structure reduces the blocky artefacts in the final reconstructed images by considering the effects of different image blocks in the reconstruction process. The proposed model has the ability to learn the nonlinear quantization function according to the input data and sending the measured values from the transmitter to the receiver with a fewer bit budget. Also, RQCSNet can learn the compressed sensing reconstruction algorithm without initial image reconstruction (that is usually the first step of reconstruction in other models), which reduces network flops. The results are presented on standard datasets of UCMerced as a remote sensing data that is the main concentration of this article, and as well as the set11 which is a dataset widely used for the benchmark of the state‐of‐the‐art models. The experiments show that RQCSNet has a better performance than existing models in terms of the quality of image reconstruction and time complexity. As a benchmark, our network has a mean PSNR improvement of about 1.2 dB in reconstructed images compared to available techniques, at a measurement rate of 0.25. The effect of adding noise to the quantized measured data from the transmitter to the receiver at the time of transmission is also investigated in this article, and it is shown that the quality of image reconstruction will be robust to the noise once adding a noise layer to the network during training. [ABSTRACT FROM AUTHOR]

This paper presents a recurrent neural network for solving nonconvex nonlinear optimization problems subject to nonlinear inequality constraints. First, the p‐power transformation is exploited for local convexification of the Lagrangian function in nonconvex nonlinear optimization problem. Next, the proposed neural network is constructed based on the Karush–Kuhn–Tucker (KKT) optimality conditions and the projection function. An important property of this neural network is that its equilibrium point corresponds to the optimal solution of the original problem. By utilizing an appropriate Lyapunov function, it is shown that the proposed neural network is stable in the sense of Lyapunov and convergent to the global optimal solution of the original problem. Also, the sensitivity of the convergence is analysed by changing the scaling factors. Compared with other existing neural networks for such problem, the proposed neural network has more advantages such as high accuracy of the obtained solutions, fast convergence, and low complexity. Finally, simulation results are provided to show the benefits of the proposed model, which compare to or outperform existing models. [ABSTRACT FROM AUTHOR]

Mappings of the stimuli effects and the input and output estimates of artificial neural networks (ANN) are obtained via combinations of nonlinear functions. This approach offers the advantages of self-learning, self-organization, self-adaptation, and fault tolerance as well as the potential for use in flood forecasting applications. Furthermore, the ANN technology allows the use of multiple variables in both the input and output layers. This capability is very important for flood calculation because the stage, discharge, and other hydrological variables often are functions of many influential variables. Herein, we propose a flood forecasting system with related application, based on ANN. This method offers better performance and efficiency. [ABSTRACT FROM AUTHOR]