Security Enhancement in Card Payment Systems

All customers who have used their credit card at an international POS terminal will have to be re-carded with a Chip +PIN credit card. A chip is a small microchip embedded in our payment card. It is encrypted so transactions are more secure on the payment card. The Chip +PIN card is a higher level of security on your card, in line with best global and local practice of security of transactions. This paper starts with an examination of a previous attempt at solving the pin entry method. It had the advantage of being simple to understand and use. Our analysis takes both the theoretical and experimental approaches reveals serious shortcomings of previous method. We suspect a security thread in this process when we enter the pin in front of our friends and family members

Preprocessing and Feature Selection on Group Structure Analysis using Entropy and Thresholding

Many real data increase dynamically in size. We have been observing in many fields that data grow with time in size. This has led to the development of several new analytic techniques. This phenomenon occurs in several fields including economics, population studies, and medical research. As an effective and efficient mechanism to deal with such data, incremental technique has been proposed in the literature and attracted much attention, which stimulates the result in this paper. When a group of objects are added to a decision table, we first introduce incremental mechanisms for three representative information entropies and then develop a group incremental rough feature selection algorithm based on information entropy.When multiple objects are added to a decision table, the algorithm aims to find the new feature subset in a much shorter time. Experiments have been carried out on eight UCI data sets and the experimental results show that the algorithm is effective and efficient

Distribution of Rationing using Automation like RFID and GSM Technology

Ration Card is very important for govt to keep log and other information about people. It is also useful for people to get govt subsidy, gas connection and even useful for residential proof. It has detail about every member in family. All the people use ration card to buy various things from ration shop like groceries,oil, kerosene. This system helps people to get this supplies at low price directly from government. This system has some drawbacks like the most of work is done by old or traditional instruments and mostly its manual work which gives many errors in measurement , and if people fail to buy their monthly subsidy then these shopkeepers sell this to other people at higher price and make money. All this happens because there is no centralized technical database for this information. The loss in this process is of people and govt who don’t have any idea about all this. Our main objective here is to make this process automatic .In this paper, proposed an Automatic Ration Materials Distribution Based on GSM (Global System for Mobile) and RFID (Radio Frequency Identification) technology instead of ration cards. When people come to ration shop they have to scan their RFID card,these cards are magnetic and has information of the beneficiary . After scanning card it is verified through database to see if it is valid or not . Whenever the user logins in system they send an otp to registered number for security purpose. If its valid then the beneficiary selects material and quantity . After receiving these material the customer pays for it. In this way we can create transparency in this process

Support Vector Machines for Human Face Detection: A Review

The computer vision drawback of face detection has over the years become a standard high-requirements benchmark for machine learning ways. Within the last decade, extremely efficient face detection systems are developed that extensively use the character of the image domain to attain correct time period performance. However, the effectiveness of such systems wouldn't be potential while not the progress within the underlying machine learning and classification ways. Now the research area of face recognition technology is much advanced because the research in this area has been conducted for more than 30 years. The main reason for the popularity of face recognition is that it can be used in the different fields like identity authentication, access control and so on. Support vector machine learning may be a comparatively recent methodology that gives a decent generalization performance. Like alternative ways, SVM learning has been applied to the task of face detection, wherever the drawbacks of the technique became evident. Analysis that specializes in accuracy found that competitive performance is feasible however training on adequately giant datasets is difficult. Others tackled the speed issue and whereas varied approximation ways created interactive response times potential, those usually came at a worth of reduced accuracy

NASA-Nearest Neighbour Algorithm with Structured Robustness Algorithm to improve Difficult Keyword Queries over Database

This is a technique to improve difficult keyword queries over databases.Estimating query performance is the job of predicting the excellence of results returned to examine in response of a query. Keyword queries on databases provide easy access to data, but often it goes through from low ranking quality. It is defined to get queries with low ranked result, quality to improve the user satisfaction. Post-retrieval predictors analyze the o of top-retrieved documents. This paper introduced a new technique to get high-performance named as NASA, this method is based on k-Nearest Neighbor (k-NN) search on the top-k results of the corrupted version of database. k-NN handles complex functions during the execution and it improve the loss of information. Simultaneously it helps to reduce the execution time. DOI: 10.17762/ijritcc2321-8169.15078

Survey on Biometric Image Sharing Using Cryptography and Diverse Image Media

Visual Secret Sharing Scheme is used to transmitting or delivering the secret images over the network. The VSS scheme has a major drawback that is it suffers from high transmission risk because the shares are like noise. As the shares are like noise that causes the attackers attention. In this paper we are using a natural-image based visual secret sharing (NVSS) scheme to reduce the transmission risk problem that occurs in VSS scheme. The NVSS scheme uses the natural images such as paintings, photographs etc as digital shares. As we are using the natural shares instead of noise like shares which reduces the transmission risk to certain limit. This scheme also uses the different media to transmit the shares

Serial Data Transmission using Visible Light Communication

Li-Fi is a label for wireless-communication system using light as a carrier instead of radio frequencies as in Wi-Fi [1]. The light waves cannot penetrate walls hence Li-Fi (Light-Fidelity) can be used in sensitive areas such as in Aircraft [13, 15]. It can be implemented using white-LED light bulbs at the downlink transmitter. In these types of devices a constant current is applied for the purpose of illumination. However, by fast and subtle variations of the current, the optical output can be made to vary at extremely high speeds. Due to this characteristic of optical current it is used in Li-Fi setup. The operational procedure is very simple- if the LED is on, then transmit a 1, if it�s off then transmit a 0. The LEDs can be switched on and off very quickly, and hence it gives nice opportunities for transmitting data. All that is required is some LEDs and a controller that code data into those LEDs. All one has to do is to increase the rate at which the LED�s flicker [2] depending upon the data needs to be encode. Further enhancements can be made in this method, like we can use an array of LEDs for parallel data transmission, or using mixtures of red, green and blue LEDs. It can alter the light�s frequency with this approach each frequency encodes a different data Channel [5]. Such advancements promise a theoretical speed of 10 Gbps � meaning you can download a full high-definition film in just 30 seconds

A Survey on Sentiment Mining

In past days before putting money into any product people used to ask judgment to their family, friend circle and colleagues and then they take the decision. In today’s world there is a boom of World Wide Web, enormous amount of data is available on internet so while purchasing a product instead of asking to people customer take decisions by analyzing electronic text. As the growth of e-commerce crowds of people encouraged to write their opinion about numerous merchandise in the form of statements/comments on countless sites like facebook,flipkart,snapdeal,amazon,bloggres,twiter,etc.This comments are the sentiments about the services expressed by users and they are categorized into positive, negative and neutral. Different techniques are use for summarizing reviews like Information Retrieval, Text Mining Text Classification, Data Mining, and Text Summarizing. Countless people write their sentiments on plenty of sites. These comments are written in random order so it may cause trouble in usefulness of the information. If someone wants to find out the impact of the usability of any product then he has to manually read all the sentiments and then classify it, which is practically burdensome task. Sentiment mining is playing major role in data mining; it is also referred as sentiment analysis. This field helps to analyze and classify the opinion of users. In this paper we will discuss various techniques, applications and challenges face by the sentiment mining

An Efficient Technique for mining Association rules using Enhanced Apriori Algorithm

There are Various mining algorithms of association rules. One of the most popular algorithm is Apriori that extracts frequent itemset from large database and getting the association rule for discovering the knowledge. this paper pitfalls the limitation of the original Apriori algorithm for wasting time for scanning the whole database searching on to the frequent itemsets, and presents an technique on Apriori by reducing that wasted time depending on scanning only some transactions whose support value is bigger than 25% of minimum Support is taken as frequent item set and is added to the frequent item sets and then rules are formed. An enhanced Apriori algorithm may find the tendency of a customer on the basis of frequently purchased item-sets The proposed algorithm is useful as a frequent item sets predictor with lower number of scans. DOI: 10.17762/ijritcc2321-8169.15063