Brain tumor is one of the harsh diseases among human community and is usually diagnosed with medical imaging procedures. Computed-Tomography (CT) and Magnetic-Resonance-Image (MRI) are the regularly used non-invasive methods to acquire brain abnormalities for medical study. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities. The combination of the Social-Group-Optimization (SGO) and Shannon's-Entropy (SE) supported multi-thresholding is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES and BRATS, and also clinical MR images obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with other segmentation procedures considered in this article. The ANFIS classifier obtained an accuracy of 94.51% on the used ISLES and real clinical images.

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To prevent the same known vulnerabilities from affecting different firmware, searching known vulnerabilities in binary firmware across different architectures is crucial. Because the accuracy of existing cross-architecture vulnerability search methods is not high, we propose a staged approach based on support vector machine (SVM) and attributed control flow graph (ACFG) at the function level to improve the accuracy using prior knowledge. Furthermore, for efficiency, we utilize the k-nearest neighbor (kNN) algorithm to prune and SVM to refine in the function prefilter stage. Although the accuracy of the proposed method using kNN-SVM approach is slightly lower than the accuracy of the method using only SVM, its efficiency is significantly enhanced. We have implemented our approach CVSkSA to search several vulnerabilities in real-world firmware images. The experimental results show that the accuracy of the proposed method using kNN-SVM approach is close to the accuracy of the method using only SVM in most cases, while the former is approximately four times faster than the latter.

The task of image-to-image translation is to generate images closer to the target domain style while preserving the significant features of the original image. This paper contends an adaptive feature fusion method for unsupervised image translation. The proposed architecture, termed as AFF-UNIT, is based on a compact network structure to further improve the quality of generated images. First of all, a feature extraction module based on an adaptive feature fusion method is proposed, which combines low-level fine-grained information and high-level semantic information to obtain feature maps with richer information. At the same time, a feature-similarity loss is proposed to guide the feature extraction module to extract features that are more conducive to improving the translation result. In addition, AFF-UNIT reuses the feature extraction module in the generator and discriminator to simplify the framework. Extensive experiments on five popular benchmarks demonstrate the superior performance of AFF-UNIT over state-of-the-art methods in terms of FID, KID, IS, and also human preference. Comprehensive ablation studies are also carried out to isolate the validity of each proposed component.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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