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: Cluster analysis & Wireless sensor network. The organization has 2136 authors who have published 3435 publications receiving 31458 citations. The organization is also known as: JIIT Noida.

Emotional Stability and Self-Esteem as Mediators Between Mindfulness and Happiness

Abstract: The present research tested a mediation model where mindfulness is indirectly related to happiness through emotional stability and self-esteem. 302 undergraduate students from a university in India participated in the study. Data were collected using self-report questionnaires of mindfulness, emotional stability, self-esteem, and happiness. Structural Equation Modeling results showed that mindfulness was associated with happiness through the mediation of emotional stability and self-esteem. Emotional stability and self-esteem fully mediated the relationship of mindfulness with happiness. In addition, emotional stability partially mediated the relationship of mindfulness with self-esteem. The results of the study shed light on how emotional stability and self-esteem are associated with the process of mindfulness-happiness relationship. Based on the findings, implications and avenues for future research are discussed.

Feature selection using Artificial Bee Colony algorithm for medical image classification

Abstract: Feature Selection in medical image processing is a process of selection of relevant features, which are useful in model construction, as it will lead to reduced training times and classification model designed will be easier to interrupt. In this paper a meta-heuristic algorithm Artificial Bee Colony (ABC) has been used for feature selection in Computed Tomography (CT Scan) images of cervical cancer with the objective of detecting whether the data given as input is cancerous or not. Starting with segmentation as a first step, performed by implementing Active Contour Segmentation (ACM) algorithm over the images. In this paper a semi-automated the system has been developed so as to obtain the region of interest (ROI). Further, textural features proposed by Haralick are extracted region of interest. Classification is performed using hybridization of Artificial Bee Colony (ABC) and k- Nearest Neighbors (k-NN) algorithm, ABC and Support Vector Machine (SVM). It is observed that combination of ABC with SVM (Gaussian kernel) performs better than combination of ABC with SVM (Linear Kernel) and ABC with K-NN classifier.

Lie symmetry based-analytical and numerical approach for modified Burgers-KdV equation

Abstract: In this work, the variable-coefficient modified Burgers-KdV equation, which arises in modeling various physical phenomena, is studied for exact and numerical solution based on Lie symmetry. The infinitesimals of the group of transformations which leaves this equation invariant are furnished along with the admissible forms of the variable coefficients. The optimal systems of one-dimensional subalgebras of the Lie symmetry algebras are determined with the adjoint action of the symmetry group. These are then used to establish new power series solution and exact solutions of variable-coefficient modified Burgers-KdV equation. Further, RK4 (e.g. Fourth Order Runge Kutta) method is applied to the reduced ODE for constructing numerical solutions of the modified Burger-KdV equation.

Bi-Objective Community Detection (BOCD) in Networks Using Genetic Algorithm

Abstract: A lot of research effort has been put into community detection from all corners of academic interest such as physics, mathematics and computer science. In this paper I have proposed a Bi-Objective Genetic Algorithm for community detection which maximizes modularity and community score. Then the results obtained for both benchmark and real life data sets are compared with other algorithms using the modularity and MNI performance metrics. The results show that the BOCD algorithm is capable of successfully detecting community structure in both real life and synthetic datasets, as well as improving upon the performance of previous techniques.