Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    Parallel method for discovering frequent itemsets using weighted tree approach
    (2009) Kumar, P.; Ananthanarayana, V.S.
    Every element of the transaction in a transaction database may contain the components such as item number, quantity, cost of the item bought and some other relevant information of the customer. Most of the association rules mining algorithms to discover frequent itemsets do not consider the components such as quantity, cost etc. In a large database it is possible that even if the itemset appears in a very few transactions, it may be purchased in a large quantity. Further, this may lead to very high profit. Therefore these components are the most important information and without which it may cause the lose of information. This motivated us to propose a parallel algorithm to discover all frequent itemsets based on the quantity of the item bought in a single scan of the database. This method achieves its efficiency by applying two new ideas. Firstly, transaction database is converted into an abstraction called Weighted Tree that prevents multiple scanning of the database during the mining phase. This data structure is replicated among the parallel nodes. Secondly, for each frequent item assigned to a parallel node, an item tree is constructed and frequent itemsets are mined from this tree based on weighted minimum support. © 2009 IEEE.
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    Estimating multiple physical parameters from speech data
    (IEEE Computer Society help@computer.org, 2016) Kalluri, S.B.; Vijayakumar, A.; Vijayasenan, D.; Singh, R.
    In this work, we explore prediction of different physical parameters from speech data. We aim to predict shoulder size and waist size of people from speech data in addition to the conventional height and weight parameters. A data-set with this information is created from 207 volunteers. A bag of words representation based on log magnitude spectrum is used as features. A support vector regression predicts the physical parameters from the bag of the words representation. The system is able to achieve a root mean square error of 6.6 cm for height estimation, 2.6cm for shoulder size, 7.1cm for waist size and 8.9 kg for weight estimation. The results of height estimation is on par with state of the art results. © 2016 IEEE.
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    Study of Wireless Channel Effects on Audio Forensics
    (Institute of Electrical and Electronics Engineers Inc., 2018) Vijayasenan, D.; Kalluri, S.B.; Sreekanth, K.; Issac, A.
    In this work, we try to study the effect of a wireless channel on physical parameter prediction based on speech data. Speech data from 207 speakers along with corresponding speaker's height and weight is collected. A three path Rayleigh fading channel with typical values of Doppler shift, path gain and path delay is utilized to create the mobile channel output audio. A Bag of Words (BoW) representation based on log magnitude spectrum is used as features. Support Vector Regression (SVR) predicts the physical parameter of the speaker from the BoW representation. The proposed system is able to achieve a Root Mean Square Error (RMSE) of 6.6 cm for height estimation and 8.9 Kg for weight estimation for clean speech. The effect of Rayleigh channel increase the RMSE values to 8.17 cm and 11.84 Kg respectively for height and weight. © 2016 IEEE.