• SUNIL KUMAR KOPPARAPU

      Articles written in Sadhana

    • Identifying the best market to sell: A cost function formulation

      Sunil Kumar Kopparapu Vikram Saxena

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      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 best where and when to sell and

      demonstrating the usefulness of the formulation on real world data.

    • Spoken Indian language identification: a review of features and databases

      BAKSHI AARTI SUNIL KUMAR KOPPARAPU

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      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.

    • Simultaneous two-sample learning to address binary class imbalance problem in low-resource scenarios

      SRI HARSHA DUMPALA RUPAYAN CHAKRABORTY SUNIL KUMAR KOPPARAPU

      More Details Abstract Fulltext PDF

      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

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