The recommender systems are the technique of processing and evaluation of user behavioral data in order to suggest the resources according to user needs. In e-learning system, it is actually used to suggest resources and relevant contents to learners. Thus e-Learning recommender system is a technique to identify requirements of learner to provide relevant learning content. In this paper current state-of-art for the e-learning recommendation system is reviewed. In addition of that recent research work in the direction of enhancing the user’s experience in online learning is also reported.
Despite numerous empirical studies and wide-spread, practical experience demonstrating the importance of rigorous documentation in software engineering, many developers continue to treat it as an “after thought.” Documentation, particularly of software architecture, should be an integral process of any development group, whether entrepreneurial, academic, or corporate. In this article the authors develop and apply the software engineering principles of Len Bass, Paul Clements, and Rick Kazman for software architecture documentation. The case study involves a relatively large-scale, academic development project aimed at supporting large-scale network security preparedness and training exercises, involving hundreds of players and a modeled network composed of hundreds of networks.
The key idea of this paper is to empirically evaluate the performance of the algorithm TTVDCA-RNR for dynamic data allocation in distributed database system (DDS).Distributed database system performance depends heavily on the allocation of data fragments at different sites.For improving the performance of distributed database system, this algorithm is working on the concept of dynamically placing the data at site(s) where it is most desirable.To study the efficiency of the algorithm TTVDCA-RNR over other similar algorithm, a sample distributed database is used.Comparative results prove that TTVDCA-RNR is more efficient algorithm and significantly improves the overall performance of the system.
The security of RSA public key cryptography and RSA digital signature relies on the assumption of the hardness of the factorization of integers.Since the inception of RSA, there have been a series of proposals for factorizing large integers.A handful of integer factorization methods rely on the fact that the prime factor having smooth p -1 value.Pollard's p -1 is a method heavily based on this fact.It takes sub-exponential time for certain integers though not suitable for all integers.This paper experiments on the abundance (or scarcity) of smooth p -1 for large primes by examining their availability and suitability.
Breast cancer is one of the leading cancer and cause of mortality among women in developing countries including India.Data mining is a field of science with the help of which we can recognize the unique patterns and markers from huge heterogeneous database responsible for particular disease.It is helpful to create awareness and to take early decisions based on evidences.In this work we applied different data mining algorithms on UCI Machine Learning Repository dataset which consists of data from case study that was conducted at the University of Chicago's Billings Hospital on the breast cancer patients.In this work models have been developed using decision tree, SVM, and neural network and the accuracy of the developed models were accessed to find the best model for this type of data.
