Medical applications have a massive footprint in human's day-to-day life. Among that, MRI has a significant role, as it incorporates a significant impact on a brain tumour. Segmenting the tumour from MRI is substa...
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Medical applications have a massive footprint in human's day-to-day life. Among that, MRI has a significant role, as it incorporates a significant impact on a brain tumour. Segmenting the tumour from MRI is substantial, but it is a time-consuming process. Both the normal and abnormal tissues found in the brain look similar, which increases the difficulty of the tumour detection process. The digital image needs to be processed to obtain an exact tumour detection result. The tumour detection process comprises five different stages, such as pre-processing, segmentation, feature extraction, feature selection, and classification. In this proposed work, hybrid wavelet Hadamard transform and grey-level co-occurrence matrix are included for feature extraction. Feature selection utilises sequential forward selection, which is an easy greedy search algorithm. This algorithm chooses only the predominant features for classification. The classification uses a hybrid support vector machine and adaptive emperor penguin optimisation. The experimental analysis shows the efficiency of the proposed work in terms of accuracy, specificity, and sensitivity values by computing the true positive, false positive, true negative, and false negative.
The warm asphalt mixture process using foam asphalt technology allows mixing and compaction at lower temperature. Nevertheless the higher air void content and incomplete coating of large aggregates are issues that nee...
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ISBN:
(纸本)9781315736754;9781138026933
The warm asphalt mixture process using foam asphalt technology allows mixing and compaction at lower temperature. Nevertheless the higher air void content and incomplete coating of large aggregates are issues that need improvement to reach the properties of hot mix asphalt. In order to improve the understanding and characterization of the bitumen foam, X-ray radiography was used to investigate the formation and decay of bitumen foam in 2D representation. image segmentation analysis was used to determine the foam bubble size distribution as a function of time. The impact of water content on the process has been studied for two penetration grade bitumen. The water content showed considerable influence on the foam quality in terms of expansion ratio and bubble size distribution. Increasing the water content in the foaming process leads to a quicker collapse of the bubbles and favors coalescence of individual bubbles.
This study proposes a generic approach which performs a series of systematic analyses by first introducing a data volume decomposition method to generate useful data features for performing semantic segmentation analy...
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This study proposes a generic approach which performs a series of systematic analyses by first introducing a data volume decomposition method to generate useful data features for performing semantic segmentationanalysis involving 3D point-cloud data. Pipeline parallelism protocol is then implemented to accelerate the deep learning model's training phase. Our proposed approach is verified by decomposing around 2.0 billion point-cloud data points, as extracted from an open-source Semantic3D dataset, into many 3D regular structures with defined numbers of voxels. Each derived 3D structure has imposed normality in their data distribution of the respective label classes. Using the optimal hyperparameters for model training, the resulting trained model achieves average overall accuracy (mOA) and average intersection over union (mIOU) values of 0.984 and 0.752, respectively, on a testing dataset having close to 800 million point-cloud data points. The results are comparable with that of other state-of-the-art models in the literature.
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