Indian Institute of Technology Kharagpur

About: Indian Institute of Technology Kharagpur is a education organization based out in Kharagpur, India. It is known for research contribution in the topics: Computer science & Dielectric. The organization has 16887 authors who have published 38658 publications receiving 714526 citations.

Credit Card Fraud Detection Using Hidden Markov Model

Abstract: Due to a rapid advancement in the electronic commerce technology, the use of credit cards has dramatically increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In this paper, we model the sequence of operations in credit card transaction processing using a hidden Markov model (HMM) and show how it can be used for the detection of frauds. An HMM is initially trained with the normal behavior of a cardholder. If an incoming credit card transaction is not accepted by the trained HMM with sufficiently high probability, it is considered to be fraudulent. At the same time, we try to ensure that genuine transactions are not rejected. We present detailed experimental results to show the effectiveness of our approach and compare it with other techniques available in the literature.

Traversable wormholes with arbitrarily small energy condition violations.

Abstract: Traversable wormholes necessarily require violations of the averaged null energy condition, this being the definition of ``exotic matter.'' However, the theorems which guarantee the energy condition violation are remarkably silent when it comes to making quantitative statements regarding the ``total amount'' of energy condition violating matter in the spacetime. We develop a suitable measure for quantifying this notion and demonstrate the existence of spacetime geometries containing traversable wormholes that are supported by arbitrarily small quantities of exotic matter.

Laser processing of materials

Abstract: Light amplification by stimulated emission of radiation (laser) is a coherent and monochromatic beam of electromagnetic radiation that can propagate in a straight line with negligible divergence and occur in a wide range of wave-length, energy/power and beam-modes/configurations. As a result, lasers find wide applications in the mundane to the most sophisticated devices, in commercial to purely scientific purposes, and in life-saving as well as life-threatening causes. In the present contribution, we provide an overview of the application of lasers for material processing. The processes covered are broadly divided into four major categories; namely, laser-assisted forming, joining, machining and surface engineering. Apart from briefly introducing the fundamentals of these operations, we present an updated review of the relevant literature to highlight the recent advances and open questions. We begin our discussion with the general applications of lasers, fundamentals of laser-matter interaction and classification of laser material processing. A major part of the discussion focuses on laser surface engineering that has attracted a good deal of attention from the scientific community for its technological significance and scientific challenges. In this regard, a special mention is made about laser surface vitrification or amorphization that remains a very attractive but unaccomplished proposition.

Drought forecasting using stochastic models

Abstract: Drought is a global phenomenon that occurs virtually in all landscapes causing significant damage both in natural environment and in human lives. Due to the random nature of contributing factors, occurrence and severity of droughts can be treated as stochastic in nature. Early indication of possible drought can help to set out drought mitigation strategies and measures in advance. Therefore drought forecasting plays an important role in the planning and management of water resource systems. In this study, linear stochastic models known as ARIMA and multiplicative Seasonal Autoregressive Integrated Moving Average (SARIMA) models were used to forecast droughts based on the procedure of model development. The models were applied to forecast droughts using standardized precipitation index (SPI) series in the Kansabati river basin in India, which lies in the Purulia district of West Bengal state in eastern India. The predicted results using the best models were compared with the observed data. The predicted results show reasonably good agreement with the actual data, 1–2 months ahead. The predicted value decreases with increase in lead-time. So the models can be used to forecast droughts up to 2 months of lead-time with reasonably accuracy.

Understanding and combating link farming in the twitter social network

Abstract: Recently, Twitter has emerged as a popular platform for discovering real-time information on the Web, such as news stories and people's reaction to them. Like the Web, Twitter has become a target for link farming, where users, especially spammers, try to acquire large numbers of follower links in the social network. Acquiring followers not only increases the size of a user's direct audience, but also contributes to the perceived influence of the user, which in turn impacts the ranking of the user's tweets by search engines.In this paper, we first investigate link farming in the Twitter network and then explore mechanisms to discourage the activity. To this end, we conducted a detailed analysis of links acquired by over 40,000 spammer accounts suspended by Twitter. We find that link farming is wide spread and that a majority of spammers' links are farmed from a small fraction of Twitter users, the social capitalists, who are themselves seeking to amass social capital and links by following back anyone who follows them. Our findings shed light on the social dynamics that are at the root of the link farming problem in Twitter network and they have important implications for future designs of link spam defenses. In particular, we show that a simple user ranking scheme that penalizes users for connecting to spammers can effectively address the problem by disincentivizing users from linking with other users simply to gain influence.