MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

School of Computer Engineering & Technology

World University root node

Engineering and Technology

International Journal of scientific research and management (IJSRM)

Evaluation of different Hypervisors Performances using Different Benchmarks

Survey on Energy Efficient \&amp; Secured Data Routing in Wireless Sensor Network

X.509 certificates enable to affirm the distinguishing proof of the parties involved in the communication. As of now, majority of individuals and communities are using X.509 certificates to demonstrate their ID during on-line exchanges, so the unwavering quality and risk level of these certificates come into a question. Hence, we have proposed a framework which assesses risk associated with X.509 Certificates with the help of certain trust criteria and characteristics. For evaluating risk related with certificate we use classification Technique with machine learning algorithm, which categorizes risk of certificate in three levels-High Risk, Medium Risk and Low risk. Our system is useful to find out risk associated with certificate while user carries out important transactions. User needs to input the certificate and system will provide risk associated with that certificate and if it is a high risk or medium risk certificate it will mention due to which parameter it bears risk. Our framework has application in browser-server communication and detecting phishing websites which have Https URLs.

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Procedia Computer Science, Elsevier

Risk Rating System of X.509 Certificates

Classification is inherently multi-objective problem. It is one of the most important tasks of data mining to extract important patterns from large volume of data. Traditionally, either only one objective is considered or the multiple objectives are accumulated to one objective function. In the last decade, Pareto-based multi-objective optimization approach have gained increasing popularity due to the use of multi-objective optimization using evolutionary algorithms and population-based search methods. Multi-objective optimization approaches are more powerful than traditional single-objective methods as it addresses various topics of data mining such as classification, clustering, feature selection, ensemble learning, etc. This paper proposes improved approach of non-dominated sorting algorithm II (NSGA II) for classification using neural network model by augmenting with local search. It tries to enhance two conflicting objectives of classifier: Accuracy and mean squared error. NSGA II is improved by augmenting backpropagation as a local search method to deal with the disadvantage of genetic algorithm, i.e. slow convergence to best solutions. By using backpropagation we are able to speed up the convergence. This approach is applied in various classification problems obtained from UCI repository. The neural network modes obtained shows high accuracy and low mean squared error. © Springer Science+Business Media Singapore 2016.

Advances in Intelligent Systems and Computing

Hybrid Multi-objective Optimization Approach for Neural Network Classification Using Local Search

A number of algorithms are available in the areas of data mining, machine learning and pattern recognition for solving the same kind of problem. But there is a little guidance for suggesting algorithm to use which gives best results for the problem at hand. This paper shows an approach for solving this problem using meta-learning. The paper uses three types of data characteristics. Simple, information theoretic, and statistical data characteristics are used. Results are generated using nine different algorithms on thirty eight benchmark datasets from UCI repository. The proposed approach uses K-nearest neighbor algorithm for suggesting the suitable algorithm. Classifier accuracy is taken as a basis for recommending the algorithm. By using meta-learning, accurate method can be recommended as per the given data, and cognitive overload for applying each method, comparing with other methods and then selecting the suitable method for use can be reduced. Thus it helps in adaptive learning methods. The experimentation shows that predicted accuracies are matching with the actual accuracies for more than 90 % of the benchmark datasets used. Thus it is concluded that the number of attributes, the number of instances, the number of classes, maximum probability of class and class entropy are playing a major role in classifier accuracy and algorithm selection for thirty eight datasets used for experimentation. © 2016 IEEE.

Proceedings of 2016 SAI Computing Conference, SAI 2016

Algorithm selection for classification problems

The use of mobilе devicеs in combination with the rapid growth of the internеt has generatеd an information ovеrload problеm. Recommendеr systеms are a necеssity to decidе which of the data are relеvant to the user. Howevеr, in mobilе devicеs therе are differеnt factors who are crucial to information retriеval, such as the location, the screеn sizе and the procеssor speеd. This papеr givеs an overviеw of the technologiеs relatеd to mobilе recommendеr systеms and a morе detailеd dеscription of the challengеs facеd

INTERNATIONAL JOURNAL OF SCIENTIFIC PROGRESS AND RESEARCH

Enhanced Role-Based Access Control Model for Mobile Recommender System

Int. J. Comput. Appl

Handwritten Marathi Consonants Recognition using Multilevel Classification

In Delay Tolerant Networks (DTNs), the information is transferred from its source to destination without end-to-end connectivity of the network. The Reputation-Based Trust Management Systems for MANETs do not apply to DTNs due to lack of end-to-end connectivity. The existing Iterative Trust Reputation Mechanism (ITRM) is effective in a detection of malicious node, however it faces from latency time, and hence, sometimes results in false detection of node considered as malicious node. The Proposed Modified Iterative algorithm for Detection of the malicious node for Delay-Tolerant Networks is based on clustering of Raters as per their ratings to Service Providers. The Raters are categorized as low priority, middle priority and high priority based on rating given to Service Provider. This has resulted into lowered amount of false positives and improvement in Service Provider reputation accuracy. The Proposed Modified Iterative algorithm for Detection of the malicious node for Delay-Tolerant Networks is compared with the existing ITRM algorithm and found that, the proposed algorithm has less time complexity and better accuracy of detection of malicious nodes. © 2015 IEEE.

IEEE Region 10 Annual International Conference, Proceedings/TENCON

An approach to detect malicious node for Delay Tolerant Networks

For privacy protection, many architectures such as trusted third party, cryptography, client side privacy etc. have been proposed. These methods are neither fully support privacy nor efficient. We have proposed limited broadcast based application and one dynamic protocol for cloaking. We have simulated user broadcast model, to achieve location privacy where users are mobile and points of interests (POI) are stationary. Second experiment is a POI broadcast model to achieve location privacy. We have also presented dynamic cloaking protocol that considers speed, direction & location of mobile user for cloaking. We have experimentally recorded the results related to performance, energy and privacy strength. It shows that our system has more social and human value along with commercial attraction. © 2016 The Authors.

Physics Procedia

Controlled Broadcast Protocol for Location Privacy in Mobile Applications

At home, on the television, the sheer number of Channels and the vast number of Programs on each Channel has itself made the task of identifying the “appropriate” program to watch difficult for the common user. There is a need of a system to generate suggestions/recommendation to the common user about which Programs to watch and when. In this paper, we propose a method and system which assists the user to choose which Programs on which Channels to watch without any inputs from the Viewer about his “Likes” or “Dislikes”. It learns from the Viewer Implicitly over time and learns all the patterns that the Viewer exhibits over the course of Television watching. © Springer International Publishing Switzerland 2016.