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.

Dispersion of Electromagnetic Waves Guided by an Open Tape Helix I

Abstract: The dispersion equation for electromagnetic waves guided by an open tape helix for the standard model of an inflnitesimally thin and perfectly conducting tape is derived from an exact solution of a homogeneous boundary value problem for Maxwell's equations. A numerical analysis of the dispersion equation reveals that the tape current density component perpendicular to the winding direction does not afiect the dispersion characteristic to any signiflcant extent. In fact, there is a signiflcant deviation from the dominant-mode sheath- helix dispersion curve only in the third allowed region and towards the end of the second allowed region. It may be concluded that the anisotropically conducting model of the tape helix that neglects the above transverse-current contribution is a good approximation to the isotropically conducting model that takes into account this contribution except at high frequencies even for wide tapes.

Analyzing Functional Microbial Diversity: An Overview of Techniques

Abstract: Ecosystem functioning is, to a large extent, dependent on the activities of microorganisms. Though comprising the largest fraction of living organisms in terms of numbers and biomass, their importance in maintenance of habitats, ecological recycling of nutrients, waste and other matter, and their crucial role in interactions between biotic and abiotic components of the environment demands that their functional role in various habitats be elucidated clearly. While the output of such studies are scattered, a limitation has also been that 99% of microorganisms are believed to belong to the uncultivable fraction. This makes it imperative that this fraction should also be addressed in functional diversity studies. The interplay between taxonomic and functional diversity, where taxonomy, to a large extent, is associated with function, mandates that the chapter also emphasize on assessment of taxonomy. The current chapter focuses on the various techniques available to study cultivable and uncultivable microorganisms (with emphasis on work pertaining to bacteria), from their functional diversity point of view.

Quantum e-commerce: a comparative study of possible protocols for online shopping and other tasks related to e-commerce

Abstract: A set of quantum protocols for online shopping is proposed and analyzed to establish that it is possible to perform secure online shopping using different types of quantum resources. Specifically, a single photon based, a Bell state based and two 3-qubit entangled state-based quantum online shopping schemes are proposed. The Bell state-based scheme, being a completely orthogonal state-based protocol, is fundamentally different from the earlier proposed schemes which were based on conjugate coding. One of the 3-qubit entangled state-based scheme is built on the principle of entanglement swapping which enables us to accomplish the task without transmission of the message encoded qubits through the channel. Possible ways of generalizing the entangled state-based schemes proposed here to the schemes which use multiqubit entangled states are also discussed. Further, all the proposed protocols are shown to be free from the limitations of the recently proposed protocol of Huang et al. (Quantum Inf Process 14:2211–2225, 2015) which allows the buyer (Alice) to change her order at a later time (after initially placing the order and getting it authenticated by the controller). The proposed schemes are also compared with the existing schemes using qubit efficiency.

A Novel Approach to Outlier Detection using Modified Grey Wolf Optimization and k-Nearest Neighbors Algorithm

Abstract: Objectives: Detecting dataset anomalies has been an interesting yet challenging area in this front. This work proposes a hybrid model using meta-heuristics to detect dataset anomalies efficiently. Methods/Statistical Analysis: A distance based modified grey wolf optimization algorithm is designed which uses the k- Nearest Neighbor algorithm for better results. The proposed approach works well with supervised datasets and gives anomalies with respect to each attribute of the dataset based on a threshold using a confidence interval. Findings: The proposed approach works well with regression as well as classification datasets in the supervised scenario. The results in terms of number of anomalies and the accuracy using confusion matrix are depicted and have been evaluated for a classification dataset considering a minority class to be anomalous in comparison to the majority class. The results have been evaluated based on varying the threshold and ‘k’ values and depending on the data set domain and data distribution the optimal parameters can be identified and used. Application/Improvements: The proposed approach can be used for anomaly detection of datasets of different domains of supervised scenario. It can also be extended to unsupervised scenario by integrating it with K-means clustering.

Randomized grey wolf optimizer (RGWO) with randomly weighted coefficients

Abstract: Meta-heuristic algorithms are quiet widely used in engineering and science. In this paper, a novel variant of grey-wolf optimizer, randomized gray-wolf optimizer, is proposed where the effect of random coefficients of the best agents are taken into consideration. The proposed variant is tested on a set of 12 standard benchmark functions and compared with grey-wolf optimizer and particle swarm optimization on different dimensional search spaces. To statistically validate and analyse the convergence behaviour of variant, wilcoxon rank sum test along with convergence graphs over 30 runs is studied. The experimental study signify that the proposed variant not only enhances the performance but has comparable convergence rate, especially in case of multi-modal problems.