Removing redundant features and improving classifier performance necessitates the use of meta-heuristic and deep learning (DL) algorithms in feature selection and classification problems. With the maturity of DL tools, many data-driven polarimetric synthetic aperture radar (POLSAR) representation models have been suggested, most of which are based on deep convolutional neural networks (DCNNs). In this paper, we propose a hybrid approach of a new multi-objective binary chimp optimization algorithm (MOBChOA) and DCNN for optimal feature selection. We implemented the proposed method to classify POLSAR images from San Francisco, USA. To do so, we first performed the necessary preprocessing, including speckle reduction, radiometric calibration, and feature extraction. After that, we implemented the proposed MOBChOA for optimal feature selection. Finally, we trained the fully connected DCNN to classify the pixels into specific land-cover labels. We evaluated the performance of the proposed MOBChOA-DCNN in comparison with nine competitive methods. Our experimental results with the POLSAR image datasets show that the proposed architecture had a great performance for different important optimization parameters. The proposed MOBChOA-DCNN provided fewer features (27) and the highest overall accuracy. The overall accuracy values of MOBChOA-DCNN on the training and validation datasets were 96.89% and 96.13%, respectively, which were the best results. The overall accuracy of SVM was 89.30%, which was the worst result. The results of the proposed MOBChOA on two real-world benchmark problems were also better than the results with the other methods. Furthermore, it was shown that the MOBChOA-DCNN performed better than methods from previous studies.

Real-time detection of Covid-19 has definitely been the most widely-used world-wide classification problem since the start of the pandemic from 2020 until now. In the meantime, airspace opacities spreads related to lung have been of the most challenging problems in this area. A common approach to do on that score has been using chest X-ray images to better diagnose positive Covid-19 cases. Similar to most other classification problems, machine learning-based approaches have been the first/most-used candidates in this application. Many schemes based on machine/deep learning have been proposed in recent years though increasing the performance and accuracy of the system has still remained an open issue. In this paper, we develop a novel deep learning architecture to better classify the Covid-19 X-ray images. To do so, we first propose a novel multi-habitat migration artificial bee colony (MHMABC) algorithm to improve the exploitation/exploration of artificial bee colony (ABC) algorithm. After that, we optimally train the fully connected by using the proposed MHMABC algorithm to obtain better accuracy and convergence rate while reducing the execution cost. Our experiment results on Covid-19 X-ray image dataset show that the proposed deep architecture has a great performance in different important optimization parameters. Furthermore, it will be shown that the MHMABC algorithm outperforms the state-of-the-art algorithms by evaluating its performance using some wellknown benchmark datasets.

Brain imaging has significantly contributed to our understanding of the cerebellum being involved in recovery after non‐cerebellar stroke. Due to its connections with supratentorial brain networks, acute stroke can alter the function and structure of the contralesional cerebellum, known as crossed cerebellar diaschisis (CCD). Data on the spatially precise distribution of structural CCD and their implications for persistent deficits after stroke are notably limited. In this cross‐sectional study, structural MRI and clinical data were analyzed from 32 chronic stroke patients, at least 6 months after the event. We quantified lobule‐specific contralesional atrophy, as a surrogate of structural CCD, in patients and healthy controls. Volumetric data were integrated with clinical scores of disability and motor deficits. Diaschisis‐outcome models were adjusted for the covariables age, lesion volume, and damage to the corticospinal tract. We found that structural CCD was evident for the whole cerebellum, and particularly for lobules V and VI. Lobule VI diaschisis was significantly correlated with clinical scores, that is, volume reductions in contralesional lobule VI were associated with higher levels of disability and motor deficits. Lobule V and the whole cerebellum did not show similar diaschisis‐outcome relationships across the spectrum of the clinical scores. These results provide novel insights into stroke‐related cerebellar plasticity and might thereby promote lobule VI as a key area prone to structural CCD and potentially involved in recovery and residual motor functioning.