Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item NLP based intelligent news search engine using information extraction from e-newspapers(Institute of Electrical and Electronics Engineers Inc., 2014) Kanakaraj, M.; Kamath S․, S.Extracting text information from a web news page is a challenging task as most of the E-News content is provided with support from backend Content Management Systems (CMSs). In this paper, we present a personalized news search engine that focuses on building a repository of news articles by applying efficient extraction of text information from a web news page from varied e-news portals. The system is based on the concept of Document Object Model(DOM) tree manipulation for extracting text and modifying the web page structure to exclude irrelevant content like ads and user comments. We also use WordNet, a thesaurus of English language based on psycholinguist studies for matching the extracted content semantically to the title of the web page. TF-IDF (Term Frequency Inverse Document Frequency) is used for identifying the web page blocks carrying information relevant to the pages title. In addition to the extraction of information, functionalities to gather related information from different web news papers and to summarize the gathered information based on user preferences have also been included. We observed that the system was able to achieve good recall and high precision for both generalized and specific queries. © 2014 IEEE.Item Sociopedia: An interactive system for event detection and trend analysis for twitter data(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2016) Kaushik, R.; Apoorva Chandra, S.; Mallya, D.; Chaitanya, J.N.V.K.; Kamath S․, S.The emergence of social media has resulted in the generation of highly versatile and high volume data. Most web search engines return a set of links or web documents as a result of a query, without any interpretation of the results to identify relations in a social sense. In the work presented in this paper, we attempt to create a search engine for social media datastreams, that can interpret inherent relations within tweets, using an ontology built from the tweet dataset itself. The main aim is to analyze evolving social media trends and providing analytics regarding certain real world events, that being new product launches, in our case. Once the tweet dataset is pre-processed to extract relevant entities, Wiki data about these entities is also extracted. It is semantically parsed to retrieve relations between the entities and their properties. Further, we perform various experiments for event detection and trend analysis in terms of representative tweets, key entities and tweet volume, that also provide additional insight into the domain. © Springer India 2016.Item An intelligent algorithm for automatic candidate selection for web service composition(Springer Verlag service@springer.de, 2018) Kedia, A.; Pandel, A.; Mohata, A.; Kamath S․, S.Web services have become an important enabling paradigm for distributed computing. Some deterrents to the continued popularity of the web service technology currently are the nonavailability of large-scale, semantically enhanced service descriptions and limited use of semantics in service life cycle tasks like discovery, selection, and composition. In this paper, we outline an intelligent semantics-based web service discovery and selection technique that uses interfaces and text description of services to capture their functional semantics. We also propose a service composition mechanism that automatically performs candidate selection using the service functional semantics, when one web service does not suffice. These techniques can aid application designers in the process of service-based application development that uses multiple web services for its intended functionality. We present experimental and theoretical evaluation of the proposed method. © Springer Nature Singapore Pte Ltd. 2018.Item A supervised learning approach for ICU mortality prediction based on unstructured electrocardiogram text reports(Springer Verlag service@springer.de, 2018) S. Krishnan, G.S.; Kamath S․, S.Extracting patient data documented in text-based clinical records into a structured form is a predominantly manual process, both time and cost-intensive. Moreover, structured patient records often fail to effectively capture the nuances of patient-specific observations noted in doctors’ unstructured clinical notes and diagnostic reports. Automated techniques that utilize such unstructured text reports for modeling useful clinical information for supporting predictive analytics applications can thus be highly beneficial. In this paper, we propose a neural network based method for predicting mortality risk of ICU patients using unstructured Electrocardiogram (ECG) text reports. Word2Vec word embedding models were adopted for vectorizing and modeling textual features extracted from the patients’ reports. An unsupervised data cleansing technique for identification and removal of anomalous data/special cases was designed for optimizing the patient data representation. Further, a neural network model based on Extreme Learning Machine architecture was proposed for mortality prediction. ECG text reports available in the MIMIC-III dataset were used for experimental validation. The proposed model when benchmarked against four standard ICU severity scoring methods, outperformed all by 10–13%, in terms of prediction accuracy. © 2018, Springer International Publishing AG, part of Springer Nature.Item Network Science based Predictive Analysis on Social Media Data(Institute of Electrical and Electronics Engineers Inc., 2023) Joshi, S.; Kamath S․, S.Traditional approaches utilizing machine learning algorithms have limitations in capturing the full depth and semantic nuances of text, hindering comprehensive analysis for tasks like opinion mining, sentiment analysis, population health analytics etc. To overcome these limitations, we propose the integration of graph analysis and Social Network Analysis (SNA) techniques to enhance the informative value of tweet analysis and facilitate the extraction of structured knowledge from textual and visual content. This work focuses on modeling user-generated content on Twitter to enable intelligent population analytics applications in the healthcare domain. Standard datasets comprising user details and their tweets are considered for the experiments, which are transformed into graph representations suitable for both structural and behavioral analytics. Additionally, a comparative study to assess the impact of varying network sizes by manipulating the number of nodes within the network is conducted. To evaluate the network properties, different centrality measures were employed and compared. © 2023 IEEE.