The recognition and categorization of ships is very significant for maritime security and defense board. The existing SAR image-oriented ship recognition techniques are not capable of real-time applications since the count of SAR sensors is sparse and the contrast of images is minimum. To cope up with certain issues, this article designs an efficient technique for ship detection and classification by introducing hybrid optimization-based deep learning technique. Here, segmentation is done using bounding box segmentation. The ships on the ocean surface are effectively detected using deep convolutional neural network, where the network is trained utilizing devised exponential mayfly optimization algorithm (EMO). The newly designed EMO is derived by the combination of the exponential weighted moving average (EWMA) and mayfly optimization algorithm (MA). The type of ships is classified effectively utilizing deep residual neural network (DRN) and the network is trained by introducing the proposed political exponential mayfly optimization algorithm (PoEMO).

Summary When compared to a cloud‐based application, fog computing allows the doctor to make better decisions in an emergency and also helps secure personal data with less latency. Parkinson's disease (PD) is a kind of brain illness, which causes stiffness, shaking, trouble in walking, talking, and so on. There is various detection techniques have been invented by researchers previously, but less accuracy is a major drawback. The main purpose of this research is the effective PD detection by the progression of optimized deep maxout network (DMN), namely, sea lion shuffled shepherd optimization (SLnSSOA)‐based DMN. Here, the developed method involves fog nodes, blockchain (BC), cloud, medical analyzer, and so on. The input data, which comes from the cloud is sent to the preprocessing phase, where data normalization is used to process it. Additionally, SLnSSOA, a combination of the sea lion optimization algorithm (SLnO) and the shuffled shepherd optimization (SSOA), is used to classify diseases using DMN, where the parameters are trained using SLnSSOA. Moreover, this technique provides an improved classification outcome. The developed method attains maximum testing accuracy of 0.908, sensitivity of 0.930, and specificity of 0.875.

This research paper develops an efficient model, named Taylor-Tunicate Swarm Algorithm-based Generative Adversarial Networks (Taylor-TSA-based GANs) for cancer prediction. The developed Taylor-TSA incorporates the Taylor series with Tunicate Swarm Algorithm (TSA) algorithm. The Yeo–Johnson (YJ) transformation is employed for the data transformation. The feature fusion is evaluated by Deep Stacked Autoencoder (Deep SAE). The fused feature is given as input to the cancer prediction done by GAN trained by Taylor-TSA. The developed model is an effective and efficient use of information with clinical data. The Taylor-TSA-based GAN is analyzed in terms of accuracy, False Positive Rate (FPR), and True Positive Rate (TPR) with the values of 0.9184, 0.1782, and 0.9246.

A novel corona virus (COVID-19) has materialized as the respiratory syndrome in recent decades. Chest computed tomography scanning is the significant technology for monitoring and predicting COVID-19. To predict the patients of COVID-19 at early stage poses an open challenge in the research community. Therefore, an effective prediction mechanism named Jaya-tunicate swarm algorithm driven generative adversarial network (Jaya-TSA with GAN) is proposed in this research to find patients of COVID-19 infections. The developed Jaya-TSA is the incorporation of Jaya algorithm with tunicate swarm algorithm (TSA). However, lungs lobs are segmented using Bayesian fuzzy clustering, which effectively find the boundary regions of lung lobes. Based on the extracted features, the process of COVID-19 prediction is accomplished using GAN. The optimal solution is obtained by training GAN using proposed Jaya-TSA with respect to fitness measure. The dimensionality of features is reduced by extracting the optimal features, which enable to increase the speed of training process. Moreover, the developed Jaya-TSA based GAN attained outstanding effectiveness by considering the factors, like, specificity, accuracy, and sensitivity that captured the importance as 0.8857, 0.8727, and 0.85 by varying training data.

Summary Alzheimer's disease (AD) is an advanced neurodegenerative disease of the brain that affects the nerve system of brain. Previously, several feature extraction and classification methods were discussed, but that methods provide high over fitting problem, which leads to minimization of detection accuracy. To overcome these issues, the multi class classification of AD diseases using bag of deep feature reduction technique and twin support vector machine classifier (TSVM) optimized with gradient decent optimizer is proposed in this manuscript for classifying the AD disease as severe AD, mild cognitive impairment, healthy control. At first, the input EEG signals are pre‐processed. To decrease the execution time and processing time with feature size, a bag of deep features reduction technique is used. The reduced feature signals are classified by optimized TSVM. The simulation process is implemented in MATLAB environment. The proposed model achieves higher accuracy 33.84%, 28.93%, 33.03%, 27.93%, higher precision 22.87%, 16.97%, 16.97%, and 36.97%, compared with the existing methods, such as piecewise aggregate approximation support vector machine (MCC‐EEG‐PAA‐SVM), convolutional neural network (MCC‐EEG‐CNN), conformal kernel‐based fuzzy support vector machine (MCC‐EEG‐CKF‐SVM), Pearson correlation coefficient‐based feature selection strategy with linear discriminant analysis classifier (MCC‐EEG‐ PCC‐LDA).