Characterization of lignite deposits of Barmer Basin, Rajasthan: insights from mineralogical and elemental analysis.

The geochemistry of fly ash produced from the combustion of coal at thermal power plants presents a significant challenge for disposal and environmental impact due to its complex mineralogical and elemental composition. The objective of this study was to investigate the mineralogical and elemental distribution of thirty lignite samples from the Barmer Basin using advanced techniques such as X-ray diffraction (XRD), X-ray fluorescence spectrometry (XRF) and inductively coupled plasma mass spectrometry (ICP-MS). XRD analysis revealed the presence of minerals such as haematite (FeO), nepheline, anhydrite, magnesite, andalusite, spinel and anatase.

"Similar query was answered earlier": processing of patient authored text for retrieving relevant contents from health discussion forum.

Online remedy finders and health-related discussion forums have become increasingly popular in recent years. Common web users write their health problems there and request suggestion from experts or other users. As a result, these forums became a huge repository of information and discussions on various health issues.

A study on use of the web for automatic answering of remedy finding questions of common users.

Background: The World Wide Web has become a huge repository of knowledge in many domains, including health problems and remedy. An intelligent system, having the capability of mining the relevant information from the web, can provide instant guidance in our basic health problems.

Objective: The first objective is to convert the free-form long user query into a structured summary.

A kernel-based approach for biomedical named entity recognition.

Support vector machine (SVM) is one of the popular machine learning techniques used in various text processing tasks including named entity recognition (NER). The performance of the SVM classifier largely depends on the appropriateness of the kernel function. In the last few years a number of task-specific kernel functions have been proposed and used in various text processing tasks, for example, string kernel, graph kernel, tree kernel and so on.

Feature selection techniques for maximum entropy based biomedical named entity recognition.

Named entity recognition is an extremely important and fundamental task of biomedical text mining. Biomedical named entities include mentions of proteins, genes, DNA, RNA, etc which often have complex structures, but it is challenging to identify and classify such entities. Machine learning methods like CRF, MEMM and SVM have been widely used for learning to recognize such entities from an annotated corpus.