This book examines the role of the Bhagavadgita in the formation of nationalist thought and analyses how the text was deployed as the central terrain of nationalist contestation and in the diverse ethico-moral mappings of the nation.

This research work proposes an implementation of adept content-based music retrieval technique that attempts to address the demands of the rising availability of digital music. The primary objective of this research work is to balance the perilous impact of non-relevant songs through progressive filtering for query by humming music information retrieval system. The PF is a technique of searching in manifolds for problem solving through reduced search space. A new strategy for empirical mode decomposition analysis is adopted, and outcomes are (...) propelled as a significant source of information for multiresolution histogram representations. Finally, the concept of PF is realized through MRH to retrieve the desired song by humming. The proposed multiresolution QBH system is evaluated on a music database of 1200 fragments, and the experimental results effectively locate the humming patterns and recite the eminence of EMD analysis for retrieving the desired song through humming. (shrink)

Biography of Ji. Hanumantarāv, 1898-1974, professor of Philosophy at University of Mysore.

The uncertainties relating to the COVID-19 pandemic continue to pose extraordinary challenges to policymakers worldwide. The recent lifting of restrictions in China raised the spectre of another wave of infections beyond its borders, which has thankfully not occurred, so far. Now, three years after the pandemic emerged, policy assumptions and responses are being re-evaluated—from whether the virus emerged as a laboratory leak, to whether vaccines have efficacy, to whether Sweden’s laissez-faire approach was superior to other countries’ ambitious interventions. We examine (...) India’s experience with COVID-19—impressionistically—to draw larger lessons for policymakers. India’s responses were a mix of hits and misses. Some measures helped contain the pandemic and assisted those in need. Other measures seemed performative—geared towards garnering glory on the global stage. Together, they throw light on the challenges of coping with a crisis in real time. (shrink)

. This paper illustrates the applications of various ensemble methods for enhanced classification accuracy. The case in point is the Pima Indian Diabetic Dataset. The computational model comprises of two stages. In the first stage, k-means clustering is employed to identify and eliminate wrongly classified instances. In the second stage, a fine tuning in the classification was effected. To do this, ensemble methods such as AdaBoost, bagging, dagging, stacking, decorate, rotation forest, random subspace, MultiBoost and grading were invoked along with (...) five chosen base classifiers, namely support vector machine, radial basis function network, decision tree J48, naïve Bayes and Bayesian network. The k-fold cross validation technique is adopted. Computational experiments with the proposed method showed an improvement of 16.14% to 22.49% in the classification accuracy compared to literature survey. Among the ensemble methods tried, MultiBoost ensemble with SVM classifier and grading ensemble with naïve Bayes showed the best performance followed by MultiBoost, stacking and grading ensemble with Bayesian classifier, rotation forest ensemble with RBF and grading and rotation forest ensemble with J48. This investigation conclusively proves the significance of cascading k-means clustering with ensemble methods in the enhanced accuracy in categorization of diabetic dataset. (shrink)