Narrow Results

The healthcare model is considered an imperative part of remote sensing of health. Finding the disease requires constant monitoring of patients’ health and the detection of diseases. In order to diagnose the disease utilizing an edge computing platform, this study develops a method called grey wolf invasive weed optimization-deep maxout network (GWIWO-DMN). The proposed GWIWO, which is developed by integrating invasive weed optimization (IWO) and grey wolf optimization (GWO), is used here to train the DMN. The distributed edge computing platform consists of four units, namely monitoring devices, first layer edge server, second layer edge server, and cloud server. The monitoring devices are used for accumulating patient information. The preprocessing and feature selection are performed in the first layer edge server. Here, the preprocessing is carried out using the exponential kernel function. The selection of features is done using Jaro–Winkler distance in the first layer edge server. Then, at the second layer edge server, clustering and classification are carried out using deep fuzzy clustering and DMN, respectively. The proposed GWIWO algorithm is used to do the DMN training. Finally, the cloud server processes the decision fusion. The proposed GWIWO-DMN outperformed with the highest true positive rate (TPR) of 89.2%, highest true negative rate (TNR) of 93.7%, and highest accuracy of 90.9%.

Pan-sharpening is a procedure to fuse the spatial detail of high-resolution multispectral images (HR-MSI) and low-resolution hyperspectral images (LR-HSI) to produce HR-MSI. Due to increase in high-resolution satellites, methods based on pan-sharpening are increasingly utilized all over the world. However, the majority of techniques consider pan-sharpening as a major issue, which hinders the discriminative ability. This work proposes an optimization-based deep model for pan-sharpening using LR-HSI and HR-MSI. Initially, the LR-HSI is input into an up-sampling mode, and the resulting image is fed into weighted linear regression. Concurrently, HR-MSI is supplied into weighted linear regression. Weighted linear regression is used to combine the upsampled LR-HSI and HR-MSI. The HR-MSI is then sent into the Deep Maxout network (DMN), which learns the priors via residual learning. Furthermore, the suggested Competitive Multi-Verse Feedback Artificial Tree (CMVFTA) strategy is used for DMN training, which is constructed by combining the Competitive Multi-Verse Optimizer (CMVO) and Feedback Artificial Tree (FAT) approaches. Finally, the DMN, LR-HSI, and HR-MSI outputs are merged together to provide a pan-sharpening image. The proposed CMVFTA-based DMN offered enhanced performance with Degree of Distortion (DD) of 0.0402 dB, Peak signal-to-noise ratio (PSNR) of 49.60 dB, Root Mean Squared Error (RMSE) of 0.330, Relative Average Spectral Error (RASE) of 0.322, Filtered Correlation Coefficients (FCC) of 0.874, Quality with no reference (QNR) of 76.19.

Rice is the most commonly consumed food in the world and several diseases affect the rice plants easily resulting in huge economic losses and decreased yield. Thus, the early stage of identification is necessary to control and alleviate the influences of pest attacks. The common disease affecting in rice is brown spot (BS). Most of the previous methods used image recognition techniques and machine-driven disease diagnosis systems to detect the crop diseases. However, these techniques are not feasible to process lots of images, time-consuming, inaccurate, and expensive. Hence, an effective approach, named shuffled Shepherd social political optimization algorithm (SSSPOA) based deep learning is developed for rice leaf infection categorization and severity percentage detection. The developed SSSPOA is the merging of shuffled shepherd social optimization (SSSO) and political optimizer (PO). Here, the input image is pre-processed by using the RoI extraction method to eliminate the unwanted noise from the image. Then, the segmentation process is done by using the DFC technique. Deep maxout network (DMN) is adopted for grading the leaf diseases into blast, bacterial blight, tungro, and BS where the training step of DMN is conducted utilizing designed SSSPOA. In addition, forecasting of severity percentage takes place using deep long short-term memory (LSTM) by taking segmented values such that the tuning mechanism of deep LSTM is done utilizing the same SSSPOA. Therefore, the presented strategy outperformed different conventional models and achieved efficient performance with a higher testing accuracy of 0.954, a sensitivity of 0.987, a specificity of 0.965, a lower mean square error (MSE) of 0.076, and a lower root mean square error (RMSE) of 0.275, respectively.

The most serious nervous system ailment, a brain tumor impairs one’s health seriously and ultimately results in death. MRI, one of the most frequently used medical imaging modalities for brain tumors, has emerged as the main diagnostic system for the treatment and study of brain tumors. It was challenging to segment and classify the many kinds of brain tumors. The swarm intelligence approach has the potential to more efficiently and effectively tackle a number of issues. Therefore, this work develops a novel model for the classification of brain tumors that includes various phases. Primarily, the input image is preprocessed via the proposed median filtering that aids in removing noises. Subsequently, segmentation is done via optimal U-Net. For precise segmentation, the weights are tuned optimally by battle royale optimization with the Bernoulli randomization (BROBR) algorithm. Then, features like the proposed local Gabor XOR pattern (PLGXP), texton features, gray level co-occurrence matrix (GLCM), and correlation features are extracted. Finally, BTC is done using a deep maxout network (DMO) that provides the final output on the absence or presence of a brain tumor.