Conference Papers

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    Exchange Rate Exposure and Usage of Foreign Currency Derivatives by Indian Nonfinancial Firms
    (Springer Science and Business Media B.V., 2018) Prasad, K.; Suprabha, K.R.
    This paper investigates the usage of currency derivatives and its impact on the exchange rate exposure. A sample of 387 nonfinancial Indian firms listed in the National Stock Exchange of India were studied for a period of 5 years from 2011–2012 to 2015–2016. The currency derivatives data was collected from the annual reports of the sample firms, and the stock price data was collected from Ace Equity and Centre for Monitoring Indian Economy (CMIE) database Prowess. The results of the study indicate that the currency forward contract is the most preferred hedging instrument among the sample firms. The Indian firms showed the lower interest in exchange-traded instruments especially currency futures. This is in spite of the growth in the third-generation innovative and low-cost derivative instruments. This study also provided evidence that hedging using currency derivatives decreased the firms’ foreign exchange exposure level, while the use of foreign currency borrowing was found insignificant in decreasing the firm’s level of currency exposure. © 2018, Springer International Publishing AG.
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    Diagnostic classification of undifferentiated fevers using artificial neural network
    (American Institute of Physics Inc. subs@aip.org, 2020) Vasudeva, S.T.; Rao, S.S.; Karanth P, N.K.; Mahabala, C.; Dakappa, P.H.; Prasad, K.
    Accurate diagnosis of undifferentiated fever case at the earliest is a challenging effort, which needs extensive diagnostic tests. Prediction of undifferentiated fever cases at an early stage will help in diagnosing the disease in comparatively lesser time and more effectively. The aim of the present study was to apply Artificial Intelligence (AI) algorithm using temperature information for the prediction of major categories of diseases among undifferentiated fever cases. This was an observational study carried out in tertiary care hospital. Total of 103 patients were involved in the study and 24-hour continuous temperature recording was done. Analysis was done using Artificial Neural Network (ANN) model based on the temperature data of each patients and its statistical parameters. Temperature datasets were labeled with the help of experienced physicians. Levenberg Marquardt error back-propagation algorithm was used to train the network. A good relation was found between the target data set and output data set, purely based on the observed 24 hr continuous tympanic temperature of the patients. An accuracy of 98.1% was obtained from ANN prediction model. The study concluded that a single noninvasive temperature parameter is sufficient to predict the major categories of diseases using ANN algorithms, from the undifferentiated fever cases. © 2020 Author(s).
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    Predictive Intelligent System Development for Disease Classification in Diagnostic Applications
    (Springer Science and Business Media Deutschland GmbH, 2024) Shrivathsa, T.V.; Rao, S.S.; Karanth, P.N.; Adiga, K.; Mahabala, M.; Dakappa, P.H.; Prasad, K.
    With ever increasing explosion in information domain and demand for highest accuracy in medical diagnosis, the existence of a reliable, accurate prediction system is the need of the hour. In this work, an effective prediction system has been developed for accurate classification of undifferentiated ailments using a unique approach. Prediction of undifferentiated diseases at an early stage always helps in better diagnosis. Illnesses like tuberculosis, non-tubercular bacterial infection, dengue fever, non-infectious diseases have regular manifestation of fever. In present work, the uniqueness lies in the use of only temperature data of the patient being referred in predicting the nature of fever, with highest degree of accuracy, instead of several self-defined parameters over limited interval of time. The system has been developed based on artificial intelligent technique, and optimization has been achieved by assessing the performance of different classifiers available. Using prediction model with classifiers, decision can take over comparative results between different classifier algorithms. A result of predictive system defines the combination of good classifier and system developed. Accuracy score and other salient parameters describe the complete picture of the system. Predictive model development in this work proved to be one of the best assistant tools to a doctor to take call over the disease crucial period. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.