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Cancer is one of the most lethal diseases in the world. A brain tumor is a form of cancer that develops in the brain's glial cells. Magnetic resonance imaging (MRI) is a prominent imaging tool for detecting brain tumors. It includes four different modalities that neurologists use to determine the location and kind of tumor. The suggested approach uses a 2D U-Net model to separate the brain tumors sub regions. To prevent excessive preprocessing and GPU utilization, the authors utilize the patching approach to partition the picture slices into distinct patches in this study. Second, they leverage the squeeze and excitation blocks to more effectively map low-level features to high-level features than a basic U-Net. The suggested technique yields DICE scores of 0.85, 0.87, and 0.90 for the three tumor categories of enhancing tumor, whole tumor, and tumor core, respectively. The results outperform the most recent approaches, including the major papers from the Brats 2019 competition.

In the field of sentiment analysis, extracting aspects or opinion targets from user reviews about a product is a key task. Extracting the polarity of an opinion is much more useful if we also know the targeted Aspect or Feature. Rule based approaches, like dependency-based rules, are quite popular and effective for this purpose. However, they are heavily dependent on the authenticity of the employed parts-of-speech (POS) tagger and dependency parser. Another popular rule based approach is to use sequential rules, wherein the rules formulated by learning from the user's behavior. However, in general, the sequential rule-based approaches have poor generalization capability. Moreover, existing approaches mostly consider an aspect as a noun or noun phrase, so these approaches are unable to extract verb aspects. In this article, we have proposed a multi-layered rule-based (ML-RB) technique using the syntactic dependency parser based rules along with some selective sequential rules in separate layers to extract noun aspects. Additionally, after rigorous analysis, we have also constructed rules for the extraction of verb aspects. These verb rules primarily based on the association between verb and opinion words. The proposed multi-layer technique compensates for the weaknesses of individual layers and yields improved results on two publicly available customer review datasets. The F1 score for both the datasets are 0.90 and 0.88, respectively, which are better than existing approaches. These improved results can be attributed to the application of sequential/syntactic rules in a layered manner as well as the capability to extract both noun and verb aspects.