Browsing by Author "Shimpi, A.V."

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    Indic Visual Question Answering
    (Institute of Electrical and Electronics Engineers Inc., 2022) Chandrasekar, A.; Shimpi, A.V.; Naik, D.
    Visual Question Answering (VQA) is a problem at the intersection of Computer Vision (CV) and Natural Language Processing (NLP) which involves using natural language to respond to questions based on the context of images. The majority of existing methods focus on monolingual models, particularly those that only support English. This paper proposes a novel dataset alongside monolingual and multilingual models using the baseline and attention-based architectures with support for three Indic languages: Hindi, Kannada, and Tamil. We compare the performance of traditional (CNN + LSTM) approaches with current attention-based methods using the VQA v2 dataset. The proposed work achieves 51.618% accuracy for Hindi, 57.177% for Kannada, and 56.061% for the Tamil model. © 2022 IEEE.
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    Support Vector Regression based Forecasting of Solar Irradiance
    (Institute of Electrical and Electronics Engineers Inc., 2022) Shimpi, A.V.; Chandrasekar, A.; Keshava, A.; Vinatha Urundady, U.
    PV power is being increasingly popular in terms of distributed energy source and derives its energy from irradiation of the sun. This irradiation differs demographically and needs to be accurately modelled for optimizing the dispatch of the source. Many methods are already in use to forecast the sun irradiation primarily based on Neural Networks and Machine learning techniques. In this paper, Support Vector based prediction is implemented and verified on a set of data. Support Vector Regressor (SVR) is a method of shifting the data points to a hyperplane and finding the correlation between the data samples. Different Kernel functions are used to define the hyperplane and their performance compared. Various combinations of input data is used to obtain the output from the regressor. Prediction metrics are used to determine the efficacy of the algorithm and based on the metrics the worst and best models for forecasting are presented. © 2022 IEEE.