Financial fraud is a serious threat that is expanding effects on the financial sector. The use of credit cards is growing as digitization and internet transactions advance daily. The most common issues in today's culture are credit card scams. This kind of fraud typically happens when someone uses someone else's credit card details. Credit card fraud detection uses transaction data attributes to identify credit card fraud, which can save significant financial losses and affluence the burden on the police. The detection of credit card fraud has three difficulties: uneven data, an abundance of unseen variables, and the selection of an appropriate threshold to improve the models' reliability. This study employs a modified Logistic Regression (LR) model to detect credit card fraud in order to get over the preceding difficulties. The dataset sampling strategy, variable choice, and detection methods employed all have a significant impact on the effectiveness of fraud detection in credit card transactions. The effectiveness of naive bayes, k-nearest neighbour, and logistic regression on highly skewed credit card fraud data is examined in this research. The accuracy of the logistic regression technique will be closer to 0.98%; with this accuracy, frauds may be easily detected. The fact that LR receives the highest classifier score illustrates how well LR predicts credit card theft.

In order to build a natural language processing system first the words are placed into a structured form that leads to a syntactically correct sentence. Syntactic analysis of a sentence is performed by parsing technique. This paper explores the novel approach that how the shift reduce parsing technique is used for translating English sentences into a grammatically correct Kannada sentences by reordering of English parse tree structure, generating and implementing phrase structure grammar(PSG) for kannada sentences. Recursive Descent Parsing technique is used to generate English phrase tree structure and terminal symbols are tagged with Kannada equivalent words then Shift-Reduce Parsing technique is used to construct a Kannada sentence. Part-of-Speech (POS) tagger is used to tag Kannada words to English words. It is implemented by using supervised machine learning approach

Context-awareness is an essential feature of pervasive applications, and runtime detection of contextual properties is one of the primary approaches to enabling context awareness. However, existing context-aware middleware does not provide sufficient support for detection of contextual properties in asynchronous environments. The contextual activities usually involve multiple context collecting devices, which are fully-decentralized and interact in an asynchronous manner. However, existing context consistency checking schemes do not work in asynchronous environments, since they implicitly assume the availability of a global clock or relay on synchronized interactions. To this end, we present the Middleware Infrastructure for Predicate detection in Asynchronous environments (MIPA), which supports context awareness based on logical time. Design and Structure of MIPA are explained in detail.

The online matrimonial market is growing rapidly, thousands of matched with matrimonial portal. The major benefits of online matrimonial markets are the ability to reach a large number of matrimonial website at low costs, to provide detailed information online, to take applications and profile matching. Also using intelligent program current information can be checked matches made more quickly. This research make the Indian online website for personalized and customization one-to-one interaction and transactions. The core component used to generate this personalized web page is the item based collaborative filtering recommendation replica. The benefits enabled by the research will be for the organization in Indian how to put together the mechanism to renovate itself to the digital market and gain competitive advantage by using electronic trade technology especially about the matrimonial market.

classification is the problem of identifying a set of categories to a new comments.To improve the efficiency of the code,quality metrics are applied for evaluation.The binary classifier,predicts the false positive rates with lesser accuracy,and limited number of classes only to predict the accuracy for classifier.To address this problem,support vector machine classifier is used,which helps in detecting the false positive rates,improving code quality and the accuracy will also increased.

Today the major problem that the people are facing is spam mails or e-mail spam. In recent years there are so many schemes are developed to detect the spam emails. Here the primary idea of the similarity matching scheme for spam detection is to maintain a known spam database, formed by user’s feedback, to block the subsequent near-duplicate spam’s. We propose a novel e-mail abstraction scheme, which considers e-mail layout structure to represent e-mails. We present a procedure to generate the e-mail abstraction using HTML content in e-mail, and this newly devised abstraction can more effectively capture the near-duplicate phenomenon of spams. Moreover, we design a complete spam detection system Cosdes (standing for Collaborative Spam Detection System), which possesses an efficient near-duplicate matching scheme and a progressive update scheme. To detect fastly near duplicates and duplicate spam mails in Cosdes, we propose a new approach SimHash.

There are several worm attacks in the recent years, this leads to an essentiality of producing new detection technique for worm attacks. In this paper we present a spectrum based smart worm detection scheme, this is based on the idea of detection of worm in the frequency domain. This scheme uses the power spectral density of the scan traffic volume and its corresponding flatness measure to distinguish the smart worm traffic from background traffic. This scheme showed better results against the smart worms and also for the c-worm detection.

Model checking automatically tests whether a model meets a given specification or not. It is a technique for verifying correctness properties of finite-state systems. One of the major problems in model checking is the state-explosion. To overcome this, a probabilistic approach called Bit-state Hashing is used to reduce the memory requirements. Bit-state hashing uses a data structure called bloom filter to store the corresponding reached states in a hash table. By enlarging a bloom filter, it improves total coverage estimation using a growth curve that approximates increased in reached states. To increase the effectiveness of the existing system, coverage estimation in model checking with sequential multiple bit-state hashing has been proposed. The sequentially repeated bit state hashing technique can outperform all other hashing methods, even for very large problem size.

ISOs are "isomorphic algorithms", which are life forms that emerged-- unplanned--from the artificial environment of the grid.Isomorphic Algorithms (better known as ISOs) are a race of programs that spontaneously evolved on the Grid, as opposed to being created by users. ISOs differ from standard programs with distinctions in their appearance and capabilities, but where they are truly unique is in their code base. While regular programs conform to the rigid structure defined by their users, ISOs have evolved, complete with a genetic code of sorts.. This inner structure of their code has allowed ISOs to develop beyond the capabilities of regular programs.. These miraculous algorithms had the capacity to evolve and change and grow at tremendous rates utilizing genetic algorithms, whereas normal programs that were intentionally written by users could only change slowly in anticipated fashions. What's REALLY important about the isomorphic algorithms id, it is revealed that indeed programs can escape the grid into the real world, essentially raising questions of what is life, sentience, the soul, etc. that humanity is no longer confined to humans, but has essentially arisen out of our digital dust and the real and digital world can become interchangeable This kind of human life form is made possible for ISOs because of digital DNA and object recognition which is made possible in isomorphic algorithms.. In this paper we aregoing to describe how an algorithm can be emergedinto human life form.

Secure group communication is a challenging task with respect to MANET’s. Since its introduction as a communication medium, wireless technology found broad application on the battlefield. Robust and reliable group communication plays a major role in developing distributed mobile application in which unannounced disconnections will occur frequently due to the mobility of the nodes which take part in mobile applications. Accompanying dramatic advances in wireless technology and the capabilities associated with small computing devices, the demand for advanced mechanisms to employ wireless technology in the battlefield continues to grow. The main objective here is to achieve robust and reliable group communication in mobile ad hoc network .Performance of the group communication is compared with the given protocols through simulation in NS-2. The analysis is made with respect to the throughput, packet transmission between source and destination. We propose four Ad hoc Routing Protocols AODV, DSR, and have given the comparative study.