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.

Unsupervised data classification using modified cuckoo search method

Abstract: Data clustering is one of the widely used data analysis methods which groups the unlabeled data into similar clusters. Classical data clustering methods under-performs to cluster multi-dimensional dataset such as micro arrays datasets. Therefore, this paper introduces a novel metaheuristic gauss-based cuckoo search clustering method to extend the capabilities of traditional clustering methods. The efficacy of proposed data clustering method has been tested on the three micro-array datasets. From the experimental results effectiveness of proposed method has been observed.

Computing solitary wave solutions of coupled nonlinear Hirota and Helmholtz equations

Abstract: In this article, we obtain the exact solutions two coupled models, one integrable system, namely coupled nonlinear Hirota (CNHI) equation and another non-integrable system, namely coupled nonlinear Helmholtz (CNHE) equation via the exp ( − Φ ( e ) ) –expansion method. The obtained travelling wave solutions are structured in rational, trigonometric and hyperbolic functions. These solutions lead to diverse types of solitary optical waves for free choices of parameters that guarantee the sustainability of such solutions. Also, 3D illustrations for the free choices of the physical parameters is provided to understand the physical explanation of the problems. These results further enrich and deepen the understanding of the dynamics of higher-dimensional soliton propagation.

An Ensemble Model for Breast Cancer Prediction Using Sequential Least Squares Programming Method (SLSQP)

Abstract: Cancer is a collection of diseases which is obsessed by uncontrolled growth of cells in the body to produce abnormal cells. Breast cancer is the most common cancer among women. In cancer, abnormal cells spread in other body part that is advance stage of cancer; at this stage survival rate is very low. So, Cancer detection at early stage is highly required to reduce the mortality rate. Here, Mortality means death because of cancer. With the improvement of innovation and machine learning techniques, cancer detection has made advances. Machine learning (ML) allows system to learn on the basis of past experiences and take decision using various statistical and probabilistic methods with minimal human intervention. This research work exhibited an ensemble model to predict the breast cancer using four machine learning techniques which are Support Vector Machine, Logistic Regression, Decision Tree and K-Nearest Neighbour. Results shows that proposed ensemble framework is more accurate in contrast of tradition single classification system. In this research work, SLSQP method is used to assign weight to each classification model and prediction of each classifier is combined using soft voting technique.