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.

Evaluating the potential of chitosan/poly(vinyl alcohol) membranes as alternative carrier material for proliferation of Vero cells

Abstract: Chitosan/poly(vinyl alcohol) (CS/PVA) blend membranes were prepared using the casting method and their physiochemical properties were analyzed using Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), differential scanning calorimetry (DSC) and X-ray diffraction (XRD). FTIR and XRD demonstrated possible hydrogen bonds between CS and PVA. The addition of PVA to CS resulted in surface roughness as analyzed by SEM. The CS/PVA blend membrane exhibited high tensile properties (81.62%) and reduced water-holding capacity (53.8%) compared to a pure CS membrane (control). Cell viability and proliferation were assessed via an MTT assay with Vero cell culture. Associated with improved physicochemical properties, the CS/PVA blend membrane promotes cell proliferation of Vero cells with high specific growth rate (0.582 day(-1)). The results demonstrate that the blending of CS and PVA could significantly alter the surface rugosity, water-holding capacity and improve the mechanical and biological properties of the membrane. Interestingly, this concept can be extended for different anchorage-dependent cell lines, as an alternative carrier material.

Performance analysis of routing protocols for vehicular adhoc networks using NS2/SUMO

Abstract: Development of intelligent transportation system is the need of all the developing countries where urbanization and industrialization is rapidly growing. VANETs are being used as a tool for improving road safety by alarming the drivers about accidents occurred ahead of them or for providing internet access to the passengers via gateways along the road. Due to highly dynamic nature of nodes in VANETs, designing a routing protocol for VANET is quite challenging compared to MANET environment. Researchers have suggested several routing mechanism for VANETs. Few routing decision are based on topology based selection whereas others have considered different parameters like location information of nodes, traffic lights etc. As no benchmarking scheme is available for choosing a routing protocol in VANET, this article gives an insight on how to choose a routing protocol depends on varying condition of traffic. Three popular protocols AODV, DSR and LAR have been chosen for analysis on varying traffic environment. All the three protocols have been critically tested for different metrics such as Throughput, Packet Delivery Ratio and Routing overhead during the simulation. Simulation is carried out with the help of open-source simulation tools NS2, a network simulator, and SUMO, a traffic simulator.

Spam Detection Using Ensemble Learning

Abstract: In our daily life, we use email and SMS many times to communicate to each other, but due to the increase of spam email and SMS, it becomes a headache for both the sender and receiver. We need spam detection tool to detect the spam, and there are many spam detection tools available in the market but they are not up to the mark because they only emphasize on individual classifier or only one or two combination of classifier. In our research, we present different combinations of four different classifiers, namely “Gaussian Naive Bayes”, “Multinomial Naive Bayes”, “Bernoulli Naive Bayes”, and “Decision Tree”. We have used voting classifier, a type of ensemble learning to calculate the accuracy of different combinations of classifiers. Results show that use of voting classifier produces more accurate prediction than individual classifier. We had also created an android application to serve the purpose. The mobile application works on client–server principle. Basically, the mobile application acts as a client which sends the data clicked by a user from mobile to server. At the server, there is machine learning script which classifies the received data and sends the prediction back to the client.