International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar -2017
p-ISSN: 2395-0072
www.irjet.net
Multi Bank ATM Family Card: INTEGRATION OF MULTI BANK MULTIPLE USER IN SINGLE CARD WITH USER BEHAVIOR MONITORING USING HMM & FORMULA VERIFICATION Mr .K.Sridharan Associate Professor Department of Information Technology Panimalar Engineering College K.G.Yuvaraaj K.C.Rahul S.Tamil Kanal S.D.Ashok Kumar Student Department of Information Technology Panimalar Engineering College
---------------------------------------------------------------------***--------------------------------------------------------------------collect data than ever before. It is said that, extracting and Abstract— The aim of this project is to use debit / credit card access for the purpose of customer’s money transactions in case of lack of money in a particular account. The multibank smart card is an application software designed to take advantage of today’s technology and reduce or avoid the time delay of amount transaction. This facilitates multi user to access his/her account with a single ATM card. User can create account and get the ATM card from the bank. He can integrate all his accounts in other banks in this single card with unique PIN number accordingly. User behavior is monitored through HMM Model and he can set up a formula based authentication. He can include all his family members accounts details also in the same card. He can withdraw cash from the account after successful authentication of the corresponding PIN number.
utilizing useful information from such huge and dynamic databases for “big data” is far from easy. Since these data are linked to real-time events, they can be employed for rescheduling or replanning activities in business applications which finally reduces the level of risk and improves profitability and efficiency of the operations. This undoubtedly can supplement traditional optimization techniques, which are a priori in nature. For instance, Zhang et al. considered a dynamic workload scheduling problem with the help of big data stored in distributed cloud services. They developed an evolutionary optimization algorithm and simulated the performance under different scenarios. In another study, Zhang et al. analyzed the cost minimization issue of moving data around geographically dispersed data. Such data migration problem is very important yet challenging as the volume of big data is growing quickly. Dou et al. Developed a service optimization model for handling big data stored in cloud systems when privacy is a critical concern (e.g., the medical data). Service quality may be compromised if a cloud server refuses to provide the data due to the privacy issue. Such optimization model can maximize the service quality and is verified by a simulation study. Another application of big data is on smart grids. Simmhan et al. predicted the demand of a cloud-based smart grid system and derived the optimal pricing strategy, based on the big data on real-time consumption. The approach is possible due to the data mining algorithm the authors developed. Owing to the importance of big data analytics for business applications, this paper is developed. With respect to the core topic on big data analytics for business operations and risk management, we organize this paper into three big sections, namely: 1) BI and data mining; 2) industrial systems reliability and security; and 3) business operational risk management (ORM). Each of these sections:
Index terms—debit/credit card, money transactions, application software, ATM card, PIN number
Introduction Information technology (IT) not only introduces convenience, but creates many new improvements which were impossible in the past. For example, advances of business intelligence (BI) methods and data mining techniques have brought huge improvements to modern business operations[10]. Nowadays, in the “big data era,” a massive amount of data is available for all kinds of industrial applications. For example, the cloud service can be considered as a data warehouse which provides a useful source of data. Wireless sensor networks [e.g., radio frequency identification (RFID), near field communications] can be used to collect useful data ubiquitously. An evolving topic on the Internet of things (IoTs), which consists of devices capable of communicating via the Internet environment, also provides a platform for gathering an enormous amount of data. In other words, it is now easier to
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