Brain tumor is the irregular growth of cells in the brain that can develop into malignant or benign tumors. However, the prediction of brain tumors is the most difficult task in the medical field due to the anatomy of...
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Brain tumor is the irregular growth of cells in the brain that can develop into malignant or benign tumors. However, the prediction of brain tumors is the most difficult task in the medical field due to the anatomy of the tumor cells. In recent years, advances in deep learning access to medical diagnostic imaging have led to greater accuracy in short time segmenting brain tumors. In this work, a novel approach based on Segmentation-based Kernel Fuzzy C-Mean (SKFCM) with penguin search optimization algorithm (PeSOA) with an Adaptive Dense Neural Network (ADNN) classifier was implemented. The MRI images are pre-processed using Bright-contrast Dynamic Histogram Equalization (BCDHE) with a weighted median filter and the multi features are extracted with the Modular linear discriminant analysis (MLDA). The Adaptive Dense Neural Network (ADNN) using a unique SKFCM with a machine learning-based penguin search optimization algorithm was used to segment brain tumors (PeSOA). The effectiveness of the proposed method was estimated based on specificity, accuracy, sensitivity, and tumor area length in vertical and horizontal locations. The proposed approach progresses the overall accuracy of 1.11%, 4.44%, and 6.18% better than CNN, ANN-fuzzy C-means, and R-CNN, respectively.
In response to the challenging conditions that arise after natural disasters, multi-robot systems are utilized as alternatives to humans for searching and rescuing victims. Exploring unknown environments is crucial in...
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In response to the challenging conditions that arise after natural disasters, multi-robot systems are utilized as alternatives to humans for searching and rescuing victims. Exploring unknown environments is crucial in mobile robotics, serving as a foundational stage for applications such as search and rescue, cleaning tasks, and foraging. In our study, we introduced a novel search strategy for multi-robot search and rescue operations. This strategy draws inspiration from the hunting behavior of penguins and combines the penguin search optimization algorithm with the Random Walk algorithm to regulate the global and local search behaviors of the robots. To assess the strategy's effectiveness, we implemented it in the ARGoS multi-robot simulator and conducted a series of experiments. The results clearly demonstrate the efficiency and effectiveness of our proposed search strategy.
Task scheduling in the cloud is the multiobjective optimization problem, and most of the task scheduling problems fail to offer an effective trade-off between the load, resource utilization, makespan, and Quality of S...
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Task scheduling in the cloud is the multiobjective optimization problem, and most of the task scheduling problems fail to offer an effective trade-off between the load, resource utilization, makespan, and Quality of Service (QoS). To bring a balance in the trade-off, this paper proposes a method, termed as crow-penguin optimizer for multiobjective task scheduling strategy in cloud computing (CPO-MTS). The proposed algorithm decides the optimal execution of the available tasks in the available cloud resources in minimal time. The proposed algorithm is the fusion of the Crow searchoptimizationalgorithm (CSA) and the penguin search optimization algorithm (PeSOA), and the optimal allocation of the tasks depends on the newly designed optimizationalgorithm. The proposed algorithm exhibits a better convergence rate and converges to the global optimal solution rather than the local optima. The formulation of the multiobjectives aims at a maximum value through attaining the maximum QoS and resource utilization and minimum load and makespan, respectively. The experimentation is performed using three setups, and the analysis proves that the method attained a better QoS, makespan, Resource Utilization Cost (RUC), and load at a rate of 0.4729, 0.0432, 0.0394, and 0.0298, respectively.
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