SUNIL KUMAR KOPPARAPU
Articles written in Sadhana
Volume 39 Issue 6 December 2014 pp 1409-1423
One of the main objectives of a farmer is to sell his final agricultural produce so as to maximize his profits. While he has several options, in terms of the markets where he can sell his produce, he is faced with a dilemma of identifying a market where he should sell his produce. There are several factors, like
the distance of the market from the farmers location
the type of produce
the transportation cost
the time taken to transport
that determine and influence the choice of market to sell. The main contributions of this paper include
the formulation of an optimization problem to identifying the
demonstrating the usefulness of the formulation on real world data.
Volume 43 Issue 4 April 2018 Article ID 0053
Spoken language is one of the distinctive characteristics of the human race. Spoken language processing is a branch of computer science that plays an important role in human–computer interaction (HCI), which has made remarkable advancement in the last two decades. This paper reviews and summarizes theacoustic, phonetic and prosody features that have been used for spoken language identification specifically for Indian languages. In addition, we also review the speech databases, which are already available for Indian languages and can be used for the purposes of spoken language identification.
Volume 45 All articles Published: 4 July 2020 Article ID 0174
Binary class imbalance problem refers to the scenario where the number of training samples in one class is much lower compared with the number of samples in the other class. This imbalance hinders the applicability of conventional machine learning algorithms to classify accurately. Moreover, many real world training datasets often fall in the category where data is not only imbalanced but also low-resourced. In this paper we introduce a novel technique to handle the class imbalance problem, even in low-resource scenarios. Inour approach, instead of, as is common, learning using one sample at a time, two samples are simultaneously considered to train the classifier. The simultaneous two-sample learning seems to help the classifier learn both intra- and inter-class properties. Experiments conducted on a large number of benchmarked datasets demonstrate the enhanced performance of our technique over the existing state of the art techniques