The prediction and diagnosis of Tuberculosis survivability has been a challenging research problem for many researchers. Since the early dates of the related research, much advancement has been recorded in several related fields. For instance, thanks to innovative biomedical technologies, better explanatory prognostic factors are being measured and recorded; thanks to low cost computer hardware and software technologies, high volume better quality data is being collected and stored automatically; and finally thanks to better analytical methods, those voluminous data is being processed effectively and efficiently. Tuberculosis is one of the leading diseases for all people in developed countries including India. It is the most common cause of death in human being. The high incidence of Tuberculosis in all people has increased significantly in the last years. In this paper we have discussed various data mining approaches that have been utilized for Tuberculosis diagnosis and prognosis. This study paper summarizes various review and technical articles on Tuberculosis diagnosis and prognosis also we focus on current research being carried out using the data mining techniques to enhance the Tuberculosis diagnosis and prognosis. Here, we took advantage of those available technological advancements to develop the best prediction model for Tuberculosis survivability

: Marine fungi are valuable and richest sources of novel natural products for medicinal and pharmaceutical industries. Nutrient depletion, competition or any other types of metabolic stress which limit marine fungal growth promote the formation and secretion of secondary metabolites. Generally secondary metabolites can be produced by many different metabolic pathways and include antibiotics, cytotoxic and cyto-stimulatory compounds. Marine fungi produce many different types of metabolite that are of commercial importance. This review paper deals about 187 novel compounds and 212 other known compounds with anticancer and antibacterial activities with a special focus on the period from 2011-2019. Furthermore, this review highlights the sources of organisms, chemical classes and biological activities (anticancer and antibacterial) of metabolites, that were isolated and structurally elucidated from marine fungi to throw a helping hand for novel drug development.

Segmentation is an important procedure in image processing. It splits a digital image into numerous sections to examine them. It is also used to make a distinction in an image. Several image segmentation techniques are available to make a smoothen image and to examine easily. The main purpose of this work is to categorize the results and compares the threshold-based, edge-based, and watershed-based image segmentation methods. This work is executed by MatlabR2016a.

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