Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Nayak, P.

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    Long Short Term Memory Networks for Lexical Normalization of Tweets
    (Institute of Electrical and Electronics Engineers Inc., 2021) Nayak, P.; Praueeth, G.; Kulkarni, R.; Anand Kumar, M.
    Lexical normalization is converting a non-standard text into a standard text that is more readable and universal. Data obtained from social media sites and tweets often contain much noise and use non-canonical sentence structures such as non-standard abbrevlatlons, skipping of words, spelling errors, etc. Hence such data needs to be appropriately processed before it can be used. The processing can be done by lexical normalization, which reduces randomness and converts the sentence structure to a predefined standard. Hence. lexical normalization can help in improving the performance of systems that use user-generated text as inputs. There are several ways to perform lexical normalization, such as dictionary lookups, most frequent replacements, etc. However, VVe aim to explore the domain of deep learning to find approaches that can be used to normalize texts lexically. © 2021 IEEE.
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    Multi-Level Statistical Model for Forecasting Solar Radiation
    (Institute of Electrical and Electronics Engineers Inc., 2022) Nayak, P.; Dash, A.; Chintawar, S.; Anand Kumar, M.
    As a substitute for conventional energy sources, Solar energy is quickly becoming a popular source of renewable energy. Various entities ranging from small households and businesses to large firms and MNCs are currently making plans on investing resources in the generation of solar energy. Thus, accurate prediction of solar radiation has become a necessity in the present scenario. Due to limitations like the unavailability of proper measuring equipment and a small number of meteorological departments, accurate prediction of solar radiation is not possible in many places around the world. This paper focuses on forecasting solar radiation using machine learning techniques. Solar radiation depends upon various natural factors, which are easier to measure, and these factors can help forecast solar radiation. This paper explores the available data to identify the various factors which affect solar radiation. Based on these factors, the paper investigates the performance of different standard regression models based on solar radiation prediction. Next, multi-level statistical models are proposed, which stack multiple standard models into layers, and the R2 scores of these custom models is compared with the R2 scores of the standard models. © 2022 IEEE.
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    Effective Information Retrieval, Question Answering and Abstractive Summarization on Large-Scale Biomedical Document Corpora
    (Springer Science and Business Media Deutschland GmbH, 2023) Shenoy, N.; Nayak, P.; Jain, S.; Kamath S․, S.; Sugumaran, V.
    During the COVID-19 pandemic, a concentrated effort was made to collate published literature on SARS-Cov-2 and other coronaviruses for the benefit of the medical community. One such initiative is the COVID-19 Open Research Dataset which contains over 400,000 published research articles. To expedite access to relevant information sources for health workers and researchers, it is vital to design effective information retrieval and information extraction systems. In this article, an IR approach leveraging transformer-based models to enable question-answering and abstractive summarization is presented. Various keyword-based and neural-network-based models are experimented with and incorporated to reduce the search space and determine relevant sentences from the vast corpus for ranked retrieval. For abstractive summarization, candidate sentences are determined using a combination of various standard scoring metrics. Finally, the summary and the user query are utilized for supporting question answering. The proposed model is evaluated based on standard metrics on the standard CovidQA dataset for both natural language and keyword queries. The proposed approach achieved promising performance for both query classes, while outperforming various unsupervised baselines. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.