In this study, a local dynamic predictive control framework combined with Levy flight (LDPC-Levy) method is proposed to assist autonomous underwater vehicles (AUV) and unmanned surface vehicles (USV) in target search ...
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In this study, a local dynamic predictive control framework combined with Levy flight (LDPC-Levy) method is proposed to assist autonomous underwater vehicles (AUV) and unmanned surface vehicles (USV) in target search missions without prior information in unknown ocean environments. The LDPC-Levy method allocates sub-regions to be searched for AUVs and simultaneously plans the appropriate position for USVs to ensure a reasonable communication distance while exploring the environment and searching for the targets. To solve the optimization problem in LDPC-Levy method, an improved remora optimization algorithm (IROA) is utilized with a restart strategy based on the concept of the firework algorithm (FWA) and simulated annealing (SA). The simulation results show that the LDPC-Levy method has significant advantages over random search and parallel line methods in terms of space exploration rate, search uniformity, and stability of the number of marked targets, while being robust against partial failure.
The uncontrolled growth of cells in a particular area is referred to as a tumor. The premature and precise identification of the tumor and its level have a straight impression on the patient's survival, treatment ...
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The uncontrolled growth of cells in a particular area is referred to as a tumor. The premature and precise identification of the tumor and its level have a straight impression on the patient's survival, treatment process, and tumor progression computation. However, in the medical field, picture segmentation and classification are more important and difficult processes. Typically, the Magnetic Resonance Imaging (MRI) modality can detect malignancy. Segmenting tumor images with respect to Cerebrospinal Fluid (CSF), Grey Matter (GM), and White Matter is the most important task in MRI identification or classification (WM). The introduction of medical image analysis based on radiology pictures is a result of the significant contributions of engineering, data sciences, and medicine. The precise and automatic segmentation of tumors affords excessive support to doctors in the medicinal area, speed detection in the treatment process, computer-aided operation, radiation treatment and so on. Thus, remora Aquila optimization (RAO)-enabled deep learning is devised for tumor classification and its severity classification. The deep learning approach is utilized for categorizing tumors as normal or abnormal as well as their severity grades. The RAO-aided deep learning system achieved improved performance with prediction error, specificity, sensitivity and testing accuracy of 0.072, 0.905, 0.925, and 0.917, respectively.
With the rapid evolution of the Internet of Things (IoT), network advancement has significantly influenced the increasing number of devices and advanced enhancements linked with it. Indeed the increasing number preval...
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With the rapid evolution of the Internet of Things (IoT), network advancement has significantly influenced the increasing number of devices and advanced enhancements linked with it. Indeed the increasing number prevalence and sophistication of emerging cyber-attacks have highlighted the necessity for designing robust security application. In this paper, the remora-based Deep Maxout Network model is Proposed. Here, the input data is acquired and forwarded to the pre-processing phase, wherein the missing value imputation approach is employed for creating a complete dataset. Later, the pre-processed data is then subjected to dimension transformation from the transformed data;the Convolutional Neural Network features are extracted, followed by feature selection based on Canberra distance. Here, detection is carried out using a Deep Maxout Network whose weights and training parameters are modified using the remora optimization algorithm. However, the proposed model has delivered superior results with a high testing ac-curacy of 0.945
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