Jaypee Institute of Information Technology

About: Jaypee Institute of Information Technology is a education organization based out in Noida, Uttar Pradesh, India. It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2136 authors who have published 3435 publications receiving 31458 citations. The organization is also known as: JIIT Noida.

Analytical Model for Optimum Signal Integrity in PCB Interconnects Using Ground Tracks

Abstract: In this paper, we present analytical models for line impedance and the coupling coefficient in the presence of additional ground tracks. We use a variational analysis combined with the transverse transmission-line technique to model interconnect lines guarded by ground tracks. Using the proposed model, it would be possible for designers to reduce crosstalk in coupled lines and obtain desired line impedance, thereby ensuring optimum signal integrity. The results obtained are verified by full-wave simulations and measurements performed on a vector network analyzer. The proposed model may find applications in the design and analysis of high-speed interconnects.

Prediction of click frauds in mobile advertising

Abstract: Click fraud represents a serious drain on advertising budgets and can seriously harm the viability of the internet advertising market. This paper proposes a novel framework for prediction of click fraud in mobile advertising which consists of feature selection using Recursive Feature Elimination (RFE) and classification through Hellinger Distance Decision Tree (HDDT).RFE is chosen for the feature selection as it has given better results as compared to wrapper approach when evaluated using different classifiers. HDDT is also selected as classifier to deal with class imbalance issue present in the data set. The efficiency of proposed framework is investigated on the data set provided by Buzzcity and compared with J48, Rep Tree, logitboost, and random forest. Results show that accuracy achieved by proposed framework is 64.07 % which is best as compared to existing methods under study.

Computational Techniques for Real-Time Credit Card Fraud Detection.

Abstract: With e-commerce becoming mainstream and a manifold increase in online transactions, security risks associated with these have become crucial concerns. In this chapter, we focus on the security issues arising out of online credit card usage. Literature in the last two and half decades has been reviewed to analyze the changing attack vectors and solution approaches to this problem. Most common attributes and open datasets of credit card transactions have been compiled to provide a starting point for new researchers. Existing fraud detection methods have been scrutinized for efficacy in addressing key challenges of fraud detection like real-time detection, concept drift, imbalanced datasets, and classifier adaptability. New directions in credit card fraud detection research have also been proposed.

Finite Element Analysis of MHD Flow of Micropolar Fluid over a Shrinking Sheet with a Convective Surface Boundary Condition

Abstract: This paper presents a numerical solution for the steady mixed convection magnetohydrodynamic (MHD) flow of an electrically conducting micropolar fluid over a porous shrinking sheet. The velocity of shrinking sheet and magnetic field are assumed to vary as power functions of the distance from the origin. A convective boundary condition is used rather than the customary conditions for temperature, i.e., constant surface temperature or constant heat flux. With the aid of similarity transformations, the governing partial differential equations are transformed into a system of nonlinear ordinary differential equations, which are solved numerically, using the variational finite element method (FEM). The influence of various emerging thermophysical parameters, namely suction parameter, convective heat transfer parameter, magnetic parameter and power index on velocity, microrotation and temperature functions is studied extensively and is shown graphically. Additionally the skin friction and rate of heat transfer, which provide an estimate of the surface shear stress and the rate of cooling of the surface, respectively, have also been computed for these parameters. Under the limiting case an analytical solution of the flow velocity is compared with the present numerical results. An excellent agreement between the two sets of solutions is observed. Also, in order to check the convergence of numerical solution, the calculations are carried out by reducing the mesh size. The present study finds applications in materials processing and demonstrates excellent stability and convergence characteristics for the variational FEM code.

Intrusion detection based on key feature selection using binary GWO

Abstract: Today's computing are network based computing. Intrusion security is one of the major challenges of such computing facilities that have to deal with every time without compromising the system performance. In practical, no such intrusion detection system is implemented that can guarantee hundred percent true detection of intrusion and threats. In this paper, we have proposed a method of network intrusion detection system using key feature selection based on binary grey wolf optimization (GWO) and neural network classifier. The proposed IDS can be installed on any strategic point of the network. By eliminating the insignificant features from dataset using GWO the size of dataset can be reduced hence reduction in training time of the classifier and storage for dataset. The simulation experiments with NSL-KDD dataset show the improved accuracy of proposed intrusion detection method with reduced feature set.