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The exponential increase in new coronavirus disease 2019 (COVID-19) cases and deaths has made COVID-19 the leading cause of death in many countries. Thus, in this study, we propose an efficient technique for the automatic detection of COVID-19 and pneumonia based on X-ray images. A stacked denoising convolutional autoencoder (SDCA) model was proposed to classify X-ray images into three classes: normal, pneumonia, and COVID-19. The SDCA model was used to obtain a good representation of the input data and extract the relevant features from noisy images. The proposed model's architecture mainly composed of eight autoencoders, which were fed to two dense layers and SoftMax classifiers. The proposed model was evaluated with 6356 images from the datasets from different sources. The experiments and evaluation of the proposed model were applied to an 80/20 training/validation split and for five cross-validation data splitting, respectively. The metrics used for the SDCA model were the classification accuracy, precision, sensitivity, and specificity for both schemes. Our results demonstrated the superiority of the proposed model in classifying X-ray images with high accuracy of 96.8%. Therefore, this model can help physicians accelerate COVID-19 diagnosis.

In this paper, we present a learning algorithm for the Elman Recurrent Neural Network (ERNN) based on Biogeography-Based Optimization (BBO). The proposed algorithm computes the weights, initials inputs of the context units and self-feedback coefficient of the Elman network. The method applied for four benchmark problems: Mackey Glass and Lorentz equations, which produce chaotic time series, and to real life classification; iris and Breast Cancer datasets. Numerical experimental results show improvement of the performance of the proposed algorithm in terms of accuracy and MSE eror over many heuristic algorithms.

A newly introduced method called Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting method is applied and extended in this study to forecast numerical values. Unlike traditional forecasting techniques which forecast only future values, our proposed method provides a new extension to correct the predicted values which is done by forecasting the estimated error. Experimental results demonstrated that the proposed method has a high accuracy both in training and testing data and outperform the state-of-the-art RNN models on Mackey-Glass, NARMA, Lorenz and Henon map datasets.

A novel approach is presented to reduced annotation based on Deep Active Learning for Arabic text detection in Natural Scene Images.- A new Arabic text images dataset (7k images) using the Google Street View service named TSVD.- A new semi-automatic method for generating natural scene text images from the streets.- Training samples is reduced to 1/5 of the original training size on average.- Much less training data to achieve better dice index : 0.84

Exploration of the deep sea and ocean in the marine industry has continued to gain interest in recent years. To get the detailed imaging of deep sea layers, marine vessels and robots are fitted with advanced imaging technologies. There are certain factors like water properties and impurities that affect the quality of the photographs captured by the underwater imaging devices. As sea water absorbs colors, so processing of sea imaging data becomes more challenging. Water light attenuation is a phenomenon that is caused by the absorbance and scattering factors. Certain studies showed that the existence of certain intrinsic shortcomings are attributed to the appearance of objects and ambient noise in underwater images. As a result, it is difficult in a real-time system to distinguish objects from their surroundings in these images. We measures the algorithms performance with respect to various aspects, effect of the hardware and software parts for underwater images and critical review of different underwater image enhancement algorithms. First, we describe some well-known techniques of spatial and frequency domains. Then, we list the existing quantitative measurements which are required to measure the quality of the enhanced image. Finally, the performance of various up-to-date existing methods is compared based on the outcomes of standard quantitative measurements, and factors such as requirements/suitability, and technical aspects, are included. Furthermore, a variety of image databases used for image contrast enhancement is discussed in detail. This study expands the scope for other researchers to understand the important characteristics of different underwater image contrast enhancement methods, and also provides future research directions.

Numerous applications, such as material handling, manufacturing, security, and automated transportation systems, use mobile robots. Autonomous navigation remains one of the primary challenges of the mobile robot industry; many new control algorithms have been recently developed that aim to overcome this challenge. These algorithms are primarily related by their adoption of new strategies for avoiding obstacles and minimizing the travel time to a target along an optimal path. In this paper, we introduce four different navigation systems for an autonomous mobile robot (PowerBot) and compare them. The four systems are based on a fuzzy logic controller (FLC). The FLC of one system is tuned by an inexperienced human (naive), while the three other FLCs are optimized through a genetic algorithm (GA), particle swarm optimization (PSO), and a human expert. We hope the comparison answers the question of which is the best controller. In other words, “who can win?,”the naive, the GA, the PSO, or the expert, in fine tuning the membership functions of the navigation and obstacle avoidance behavior of the mobile robot? To answer this question, we used four different techniques for optimization (the naive FLC, GA, PSO, and FLC-expert) and used many criteria for comparison, whereas other research papers have dealt with two techniques at a time.

Pedestrian path prediction is an emerging topic in the crowd visual analysis domain, notwithstanding its practical importance in many respects. To date, the few contributions in the literature proposed quite straightforward approaches, and only a few of them have taken into account the interaction between pedestrians as a paramount cue in forecasting their potential walking preferences in a given scene. Moreover, the typical trend was to evaluate the proposed algorithms on sparse scenarios. To cope with more realistic cases, in this paper, we present an efficient approach for pedestrian path prediction in densely crowded scenes. The proposed approach initiates by extracting motion features related to the target pedestrian and his/her neighbors. Second, in order to further increase the representativeness of the extracted motion cues, an autoencoder feature learning model is considered, whose outcome finally feeds a Gaussian process regression prediction model to infer the potential future trajectories of the target pedestrians given their walking records in the scene. Experimental results demonstrate that our framework scores plausible results and outperforms traditional methods in the literature.

In this paper, we apply the Elman Neural Network (ENN) trained with Grey Wolf Optimizer (GWO) for time series predictions and data classification. The Grey Wolf Optimizer algorithm optimizes the network parameters. In order to evaluate the performance of the proposed method, we have carried out some experiments on two data sets: Mackey Glass, and Breast Cancer. We also give simulation examples to compare the effectiveness of the model with five known meta-heuristics methods in the literature. The results show that the GWO-ENN model produces a better generalization performance.

In this paper, a tree-based encoding method is introduced to represent the Beta basis function neural network. The proposed model called Flexible Beta Basis Function Neural Tree (FBBFNT) can be created and optimized based on the predefined Beta operator sets. A hybrid learning algorithm is used to evolving FBBFNT Model: the structure is developed using the Extended Genetic Programming (EGP) and the Beta parameters and connected weights are optimized by the Opposite-based Particle Swarm Optimization algorithm (OPSO). The performance of the proposed method is evaluated for benchmark problems drawn from control system and time series prediction area and is compared with those of related methods.

This paper proposes a hierarchical multi-dimensional differential evolution (HMDDE) algorithm, which is an automatic computational frame work for the optimization of beta basis function neural network (BBFNN) wherein the neural network architecture, weights connection, learning algorithm and its parameters are adapted according to the problem. In the HMDDE-designed neural network, the number of individuals of the population multi-dimensions is the number of beta neural networks. The population of HMDDE forms multiple beta networks with different structures at the higher level and each individual of the previous population is optimized at a lower hierarchical level to improve the performance of each individual. For the beta neural network consisting of m neurons, n individuals (different lengths) are formed in the upper level to optimize the structure of the beta neural network. In the lower level, the population within the same length is to optimize the free parameters of the beta neural network. To evaluate the comparative performance, we used benchmark problems drawn from identification system and time series prediction area. Empirical results illustrate that the HMDDE produces a better generalization performance.

In this paper, we apply the Beta Basis Function Neural Network (BBFNN) trained with cuckoo search (CS) for time series predictions. The cuckoo search algorithm optimizes the network parameters. In order to evaluate the effectiveness of the proposed method, we have carried out some experiments on four data sets: Mackey Glass, Lorenz attractor, Henon map and Box-Jenkins. We give also simulation examples to compare the effectiveness of the model with the other known methods in the literature. The results show that the CS-BBFNN model produces a better generalization performance.

This paper presents an application of swarm intelligence technique namely Artificial Bee Colony (ABC) to design the design of the Beta Basis Function Neural Networks (BBFNN). The focus of this research is to investigate the new population metaheuristic to optimize the Beta neural networks parameters. The proposed algorithm is used for the prediction of benchmark problems. Simulation examples are also given to compare the effectiveness of the model with the other known methods in the literature. Empirical results reveal that the proposed ABC-BBFNN have impressive generalization ability.

Many methods for solving optimization problems, whether direct or indirect, rely upon gradient information and therefore may converge to a local optimum. Global optimization methods like Evolutionary algorithms, overcome this problem although these techniques are computationally expensive due to slow nature of the evolutionary process. In this work, a new concept is investigated to accelerate the differential evolution. The opposition-based DE uses the concept of opposite number to create a new population during the learning process to improve the convergence rate of generalization performance of the beta basis function neural network. The proposed algorithm uses the dichotomy research to determine the target solution. Detailed performance comparison of ODE-BBFNN with learning algorithm on benchmarks problems drawn from regression and time series prediction area. The results show that the ODE-BBFNN produces a better generalization performance.

In this paper, we propose a differential evolution algorithm based design for the beta basis function neural network. The differential Evolution algorithm has been used in many practical cases and has demonstrated good convergences properties. The differential evolution is used to evolve the beta basis function neural networks topology. Compared with the traditional genetic algorithm, the combined approach proves goodly the difference, including the feasibility and the simplicity of implementation. In the prediction of Mackey-Glass chaotic time series, the networks designed by the proposed approach prove to be competitive, or even superior, to the traditional learning algorithm for a multi-layer Perceptron network and radialbasis function network. Therefore, designing a set of BBFNN can be considered as solution of a two-optimisation problem.