Sourabh Deorah

Sourabh Deorah

United States
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About

I am the founder and CEO of Advantage Club, a global platform for employee engagement…

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Experience

  • Advantage Club Graphic

    Advantage Club

    Gurgaon

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    Gurgaon

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    Bellevue

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    Cupertino

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    Culver City, Los Angeles

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    Redmond, Washington

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    Bengaluru Area, India

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    Patiala Area, India

Education

Publications

  • Evaluation and Extension to the Duckworth Lewis Method: A Dual Application of Data Mining Techniques

    Proceedings of the 11th IEEE International Conference on Data Mining Workshops (ICDMW 2011)

    This paper deals with the evaluation of the Duckworth Lewis method, identifying its limitation, and devising a modification to address these limitations. The Duckworth Lewis method, or D/L method, was created by Frank Duckworth and Tony Lewis. The International Cricket Council (ICC) adopted D/L method in 1999 to address the issue of delayed one-day cricket matches due to interruptions such as inclement weather conditions, poor light and floodlight failures, and crowd problems. We have attempted…

    This paper deals with the evaluation of the Duckworth Lewis method, identifying its limitation, and devising a modification to address these limitations. The Duckworth Lewis method, or D/L method, was created by Frank Duckworth and Tony Lewis. The International Cricket Council (ICC) adopted D/L method in 1999 to address the issue of delayed one-day cricket matches due to interruptions such as inclement weather conditions, poor light and floodlight failures, and crowd problems. We have attempted to identify the shortcomings in the existing Duckworth Lewis method using data mining algorithms such as Random Forests and C4.5.

    We have also shown that the p-values and other data mining techniques serve a dual purpose of not only evaluating whether systems such as D/L method have been exploited by taking advantage of their properties such as simplicity, but also devising alternate and robust approaches (or models).

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  • SEAS-Expert system for advising academic major

    IEEE Xplore, International Advance Computing Conference’2010

    Most students in India choose their undergraduate major solely on the basis of persisting trends in the society. Due to the lack of a holistic guidance system, students often end up making choices solely on the basis of the above parameter, which in eventuality, may fail to align with the student’s actual interest and inherent aptitude towards a particular major. In this paper we propose an expert system-SAES which aims to provide intelligent advice to the student as to which major he/she…

    Most students in India choose their undergraduate major solely on the basis of persisting trends in the society. Due to the lack of a holistic guidance system, students often end up making choices solely on the basis of the above parameter, which in eventuality, may fail to align with the student’s actual interest and inherent aptitude towards a particular major. In this paper we propose an expert system-SAES which aims to provide intelligent advice to the student as to which major he/she should opt. SAES acquires knowledge of academic performances as well as explicit and implicit interests of the candidate. Knowledge representation in SAES is done by the use of a combination of case based and rule based reasoning. SAES draws inferences on the basis of acquired knowledge and also takes into account the degree of dilemma faced by the candidate and the time he/she takes to decide the interest areas. SAES then recommends the most suitable majors for each candidate, which are further classified as strong, mild and weak on the basis of calculated relative probabilities of success. At the end, we analyze results of the test conducted on a working prototype of SAES.

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