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.

A Comparative Study of Breast Cancer Diagnosis Using Supervised Machine Learning Techniques

Abstract: Cancer is a class of diseases, which is driven by change in cells of the body and increase beyond normal growth and control. Breast cancer is one of the frequent types of cancer. Prognosis of breast cancer recurrence is highly required to raise the survival rate of patient suffering from breast cancer. With the advancement of technology and machine learning techniques, the cancer diagnosis and detection accuracy has improved. Machine learning (ML) techniques offer various probabilistic and statistical methods that allow intelligent systems to learn from reoccurring past experiences to detect and identify patterns from a dataset. The research work presented an overview of evolve the machine learning techniques in cancer disease by applying learning algorithms on breast cancer Wisconsin data –Linear regression, Random Forest, Multi-layer Perceptron and Decision Trees (DT). The result outcome shows that Multilayer perceptron performs better than other techniques.

Mixed convection flow along an inclined permeable plate: effect of magnetic field, nanolayer conductivity and nanoparticle diameter

Abstract: Steady, two-dimensional mixed convection boundary layer flow of an incompressible $${\rm Al}_2{\rm O}_3$$ –water nanofluid along an inclined permeable plate in the presence of transverse magnetic field has been examined numerically. The governing equations (Boussinesq approximation) with associated boundary condition are solved using FEM for nanofluid containing spherical-shaped nanoparticles having volume fraction ranging from 1 to 4 %. Static-based model for calculating the effective thermal conductivity at 300 K, proposed by Leong et al. (J Nanopart Res 8:245–254, 2006) and Murshed et al. (Int J Therm Sci 47:560–568, 2008) has been implemented. Effect of various pertinent parameters with different classical and experimental models for effective dynamic viscosity is discussed.

Situation recognition in sensor based environments using concept lattices

Abstract: A variety of sensors are available nowadays for fine grain continuous monitoring of our environments in many desired ways. Comprehension of streams of data from deployed sensors in a meaningful way is critical to usability of the sensors. One such comprehension is context and situation which may affect our actions and decisions. Context is deduced from the sensor data using probabilistic methods like maximum likelihood estimation and Bayesian probabilities. Possible situations are abstracted using deduced contexts. Event trees, template and rule based methods have been used for deriving situations from contexts. Lattices are constructed using formal concept analysis methods for representation and recognition of situations. When the context information is either noisy or incomplete, set of Implication and Association Rules are derived from the lattice and used for situation recognition. For illustration, Situation of an elderly person living alone in a house, deployed with various sensors is recognized using the above technique.

Cluster based load balancing in cloud computing

Abstract: For a cloud datacenter the biggest issue is how to tackle billions of requests coming dynamically from the end users. To handle such requests efficiently and effectively, there is a need to distribute the load evenly among the cloud nodes. To achieve this goal, various load balancing approaches have been proposed in the past years. Load balancing strategies aim at achieving high user satisfaction by minimizing response time of the tasks and improving resource utilization through even and fair allocation of cloud resources. The traditional Throttled load balancing algorithm is a good approach for load balancing in cloud computing as it distributes the incoming jobs evenly among the VMs. But the major drawback is that this algorithm works well for environments with homogeneous VMS, does not considers the resource specific demands of the tasks and has additional overhead of scanning the entire list of VMs every time a task comes. In this paper, these issues have been addressed by proposing an algorithm Cluster based load balancing which works well in heterogeneous nodes environment, considers resource specific demands of the tasks and reduces scanning overhead by dividing the machines into clusters. Experimental results have shown that our algorithm gives better results in terms of waiting time, execution time, turnaround time and throughput as compared to existing throttled and modified throttled algorithms.