Clustering is one of the most researched areas of data mining applications in the contemporary literature. The need for efficient clustering is observed across wide sectors including consumer segmentation, categorization, shared filtering, document management, and indexing. The research of clustering task is to be performed prior to its adaptation in the text environment. Conventional approaches typically emphasized on the quantitative information where the selected features are numbers. Efforts also have been put forward for achieving efficient clustering in the context of categorical information where the selected features can assume nominal values. This manuscript presents an in-depth analysis of challenges of clustering in the text environment. Further, this paper also details prominent models proposed for clustering along with the pros and cons of each model. In addition, it also focuses on various latest developments in the clustering task in the social network and associated environments.
Lung carcinoma, generally known as lung cancer, is the most common cause of cancer which is related to mortality worldwide. Lung carcinoma is an extremely complex problem to solve and Lung cancer patients appear to be the most vulnerable to SARS-CoOVID-19 infection early discovery, on the other hand, has a high rate of survivability. Lung carcinoma detection in computed tomography (CT) has emerged as an emerging research subject in the field of medical imaging systems in recent years. The ability to accurately detect the size and location of lung cancer plays a critical role in lung cancer diagnosis. As a result, there is a requirement to rapidly read, detect, classify and evaluate CT scans. In this paper, we suggest a method for detecting and classifying lung nodules (or lesions) using a multi-strategy system. It has two parts: nodule detection (finding nodules) and classification (classifying nodules into Benign / non-cancerous or Malignant / cancerous). Lung CT scan images are utilized to detect and classify lung nodules in this work. U-Net architecture is used to segment CT scans, while VGG Net is tested on 3D images derived from LUNA 16 and LIDC - IDRI. The U-Net and the VGG-Net results are combined in the final findings.
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