Author
Lydia J Gnanasigamani
Bio: Lydia J Gnanasigamani is an academic researcher from VIT University. The author has contributed to research in topics: Computer science & Supply chain. The author has co-authored 1 publications.
Papers
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TL;DR: A deep learning methodology for the generation of the automatic summaries of the medical case reports is presented and a proposed fine-tuned summarizer on the test data set generated a mean precision and Rouge-1 Score of 0.2803.
Abstract: A medical case report gives medical researchers and healthcare providers a thorough account of the symptoms, treatment, and diagnosis of a specific patient. This clinical data is essential because they aid in diagnosing novel or uncommon illnesses, analyzing specific medical occurrences, and enhancing knowledge of current medical education. The summary of the medical case report is needed so that one can decide on further reading as going through the entire contents of a medical case report istime-consuming. In this paper, we present a deep learning methodology for the generation of the automatic summaries of the medical case reports. The final proposed fine-tuned summarizer on the test data set generated a mean precision of 0.4481 and Rouge-1 Score of 0.2803.
TL;DR: This work intends to identify the similarity between regions in the geographical area using Rough Set methodology so that similar crime-fighting strategies for the neighbours and alleviate the crime.
Abstract: Crime analysis has been carried out to find out patterns and associations in crime incidents. A few of the different latitudes that research has been carried out are the prediction of crime rate, sociological impacts of crime, the contribution of socio-economic factors to the crime and finding the places where the frequency of crime is unusually high. GIS and spatial information have evolved as an inherent part of the crime data as the information is made public by the policing agencies. ‘Crime mapping' refers to mapping a crime to a particular place. Geography or the spatial information of crime plays an important role in the analysis of crime. Previous research have documented the spatial importance in identifying the hotspots and showing crime distribution in a particular geography. This work intends to identify the similarity between regions in the geographical area using Rough Set methodology. By doing so, we can prepare similar crime-fighting strategies for the neighbours and alleviate the crime.