Chandigarh University

About: Chandigarh University is a education organization based out in Mohali, India. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 1358 authors who have published 2104 publications receiving 10050 citations.

Energy-Efficient Model for Recovery from Multiple Cluster Nodes Failure Using Moth Flame Optimization in Wireless Sensor Networks

Abstract: For transmission and collection of sensed data, it is essential that the connectivity among deployed sensor nodes in WSNs. The maintenance of network connectivity is a challenging task in harsh environmental conditions when participating nodes’ failures lead to the network’s disjoint partitions. To improve the connectivity and coverage with energy efficiency for the partitioned network, optimal positioning of sensor nodes has been performed based on the moth flame optimization algorithm (OPS-MFO). In the anchor node, the relay nodes have exploited in the proposed model—two phases involved in the proposed model, such as the inter-partition phase and intra-partition phase. For intra-partitioning and inter-partitioning, all sensor nodes and relay nodes’ positions have been estimated using the moth flame optimization algorithm for better connectivity. The proposed model is outperformed based on the experimental analysis and evaluation by comparing them with the existing algorithms.

Vitiligo: successful combination treatment based on oral low dose cytokines and different topical treatments.

Abstract: The current treatments for Vitiligo are not completely satisfactory in terms of clinical, aesthetic and compliance results for patients. Recently, combination therapies had been introduced with positive results. In this paper the combination between systemic oral treatment with Low Dose Cytokines in association with other topical treatments was evaluated. Positive results were obtained both with Low Dose Cytokines alone or in association with microphototherapy with positive percentage of skin repigmentation varying between 74% and 90%. Collected data allow the authors to affirm that the treatment with oral low dose SKA drugs is efficacious per se and highly efficacious in association with targeted phototherapy.

A survey on Vehicular Cloud Computing and its security

Abstract: Vehicular networking has a significant advantages in the today era. It provides desirable features and some specific applications such as efficient traffic management, road safety and infotainment. The vehicle consists of comparatively more communication systems such as on-board computing device, storage and computing power, GPS etc. to provide Intelligent Transportation System (ITS). The new hybrid technology known as Vehicular Cloud Computing (VCC) has great impact on the ITS by using the resources of vehicles such as GPS, storage, internet and computing power for instant decision making and sharing information on the cloud. Moreover, the paper not only present the concept of vehicular cloud but also provide a brief overview on the applications, security issues, threats and security solution for the VCC.

National Identity Predictive Models for the Real Time Prediction of European School’s Students: Preliminary Results

Abstract: An experimental study is conducted to predict the real time national identity (national or immigrants) of the students based on their responses in information and communication technology (ICT) survey held in European schools. All the experiments are conducted in SPSS IBM modeler version 18.1. The target datasets were collected by ESSIE (SMART 2010/0039) during the big survey at levels 3 of schools ISCED (International Standard Classification of Education) in the year 2011. The auto classifier node selected 5 supervised machine learning classifiers filtering out of 8 classifiers. To predict the national identity of students in academic school, the highest accuracy 96.6% is achieved by decision tree C5 with filtering of 46 features out of total 156 and to predict the national identity of students in vocational school, the uppermost accuracy 94.3% is achieved by Tree-AS with reduction of total 41 features out of total 172. Hence, to predict the national identity, self-reduction and auto classifier stabilized only 46 features for C5 Tree and 41 features for Tree-AS. The findings of paper also signify that C5 classifier outperformed the Logistic Regression (LR) and Tree-AS after feature reduction at academic schools. Further, Tree-AS also outperformed the Bayesian network (BN), linear support vector machine (LSVM) and LR after feature reduction at vocational schools.