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

Data redundancy is quite common in wireless sensor networks (WSNs) where nodes are deployed densely. The reason behind such deployment is to achieve reliability from communication failure. Communication failure happens when particular node transmitting data fails. In WSN there is no other way than keeping redundant nodes to solve communication failure problem. If redundant nodes are available then at the time of node failure the data of failed node can be recovered from its redundant nodes. Though we can achieve reliability through redundant nodes presence of redundant nodes will generate more number of redundant packets which will consume more energy of network. Because nodes which are densely deployed will sense the same information and send it to sink node and sink will waste its energy in processing redundant data and also redundancy will generate heavy traffic in network. Hence, there is a need to trade-off between energy conservation and reliability. To do this trade-off we need to find optimization point of redundancy in WSN. So that reliability and energy conservation both will be maintained. In this paper we have used clustering-based load shifting policy (LSP) to eliminate redundancy up to an adequate level to achieve optimization point of redundancy. Data aggregation eliminates redundancy from WSN. We are performing data aggregation at two levels and at the same time we are keeping redundant nodes up to 50 % to achieve reliability. In this paper, we have done comparison of traditional cluster-based data aggregation with our LSP-based data aggregation. Simulation result shows that LSPDA has lesser average energy consumption and longer lifetime than traditional cluster-based data aggregation method. © Springer Science+Business Media Singapore 2016.

World University root node

Engineering and Technology

Advances in Intelligent Systems and Computing

Energy efficient data aggregation technique using load shifting policy for wireless sensor network

With an ever expanding human population the number of vehicles on road is also increasing. The traditional natured traffic management system and limited road lengths available, it's time that some improvisation and thought is laid upon on this subject as well. The evolution of Information Technology era is leading to the Internet of Things (IoT) and accordingly there are several technologies that have come into existence like Vehicular Ad-hoc Network (VANET). In this paper an innovative idea for traffic management system using radio frequency identification (RFID) tags and RFID Reader is described which has the ability to monitor and manage traffic as well as can be used to detect the collisions. It also introduces a Smart city framework and explains the storage of vehicle history and information related, automated traffic violation monitoring, and automated fine/challan delivery. It also describes how RFID tracker applications for mobile and computers can be modified by software upgrades and create real time monitoring systems. Resulting system would be far cheaper and reliable than the current CCTV camera based system and it could be used as a platform for various other applications. © 2016 ACM.

ACM International Conference Proceeding Series

An efficient and economical solution for future traffic management system using RFID

International Journal of Advance Engineering and Research Development (IJAERD)

Ontology Based Information search platform for comparative analysis of Educational Data using NLP

Data stream classification is a challenging task. For real-time, data concepts of instances keep varying with time, such as weather prediction or intrusion detection etc. Classification is a supervised technique to mine useful patterns from these real data. There is a need to mine knowledge from these large data streams with techniques those provide accurate results and memory efficient. Using a set of classifiers i.e. ensemble classifier than a single one, proves to be more efficient. In this paper, Revised Accuracy Updated Ensemble(RAUE) classifier algorithm is proposed. In this algorithm, the data streams are processed in blocks. The ensemble used in RAUE uses Hoeffding Option Trees(HOT) as basic classifier set. The experimental evaluation and comparison of the proposed RAUE with existing ensemble algorithms is carried out. Out of all the compared algorithms, RAUE shows increase in accuracy and also uses less memory for identifying class labels for instances.

International Journal of Emerging Technologies in Computationaland Applied Sciences (IJETCAS)

DATA STREAM CLASSIFICATION THROUGH ENSEMBLE CLASSIFIER USING HOEFFDING OPTION TREES

Classification is important task of data mining used to extract knowledge from huge volume of data. By nature, classification is multi-objective problem, as it required optimization of multiple objectives simultaneously like accuracy, sensitivity, squared error, precision etc. Traditionally, evolutionary algorithms were used to solve multi-objective classification problem by considering it as single-objective problem, but this approach gives single solution to problem. Therefore, multi-objective evolutionary algorithms are used to solve classification problem. In this paper, we have used Pareto approach to optimize neural network for classification. Non-dominated sorting genetic algorithm is used to simultaneously optimize accuracy and mean squared error objectives of neural network along with local search. As slow convergence to optimal solutions is major disadvantage of evolutionary algorithm. To speed up convergence to optimal solutions hybrid technique is adopted by augmenting evolutionary technique with local search algorithm. This proposed approach gives set of Pareto optimal solutions which represent learned neural network models.

Journal of Innovation in Electronics and Communication Engineering

Hybrid NSGA II Approach for Neural Network Classification

—Virtualization refers to the abstraction between the hardware resources and the software running on a system, making it possible to run multiple operating systems on one physical machine, each one completely separated from the other. It has both technological as well as economical roots, and its adoption is still being driven accordingly, in a race to balance between these two aspects in order to get the most out of it. Enough is being said about the efficiency and the cost savings of this technology, but very little about the security implications it might bring. Many of the security issues in virtualization arise due to the difficulty of inspecting and monitoring a virtual machine continuously, as well as the quality and usefulness of the information that can be monitored and extracted. This monitoring can be achieved by virtual machine introspection (VMI) which is leveraged from hypervisor (VMM-virtual machine monitor). Balancing between security and efficiency cannot be achieved using Network-Based IDS or Host-Based IDS but by VMI. Keywords— Virtual machine(VM), Virtual machine introspection(VMI), Intrusion detection System(IDS), hypervisor security, cloud security, virtual machine monitor(VMM)

IJISET - International Journal of Innovative Science, Engineering & Technology

Securing Virtual Machine via Virtual Machine Introspection

Spam Email, also known as junk email , is a subset of electronic spam involving nearly identical messages sent to numerous recipients by email. The messages may contain disguised links that appear to be for familiar websites but in fact lead to phishing web sites or sites that are hosting malware. Spam email may also include malware as scripts or other executable file attachments. Spam is any unwanted and harmful mail. Separation of spam from normal mails is essential. This paper surveys different spam email filtering techniques. The different techniques are Machine learning based, list based, content based and hybrid or other. Machine learning based, is mostly used because of high accuracy and mathematical support.

International Journal of Engineering Trends and Technology (IJETT)

Survey on Spam Filtering Techniques and Mapreduce

International Journal on Recent and Innovation Trends in Computing and Communication

A Survey on Classification of Photo Aesthetics Based on Emotion

Owner of data driven to outsource their composite data management systems from local positions to thecommercial open cloud for prodigious economic savings and flexibility .Keyword search on cloud having numerousadvantages, as the owner of data will get powerful tool to precisely describe his informational need . But forprotecting data confidentiality, sensitive data have to be encrypted before outsourcing, which obsoletes traditionalutilization of data based on keyword query search. Thus, permitting an encrypted cloud data search service is of vitalprominence. In this paper we define and try to solve the stimulating problem of authentication and access control formulti keyword ranked search over encrypted data in cloud computing, and to establish a set of strict confidentialityrequirements for such a secure cloud data utilization system. Among various multi-keyword semantics, and to choosethe efficient similarity measure of “coordinate matching,” i.e., as many matches as possible, to capture the relevance ofdata documents to the search query. Further use “inner product similarity” to quantitatively evaluate such similaritymeasure. In this project first to propose a basic idea for the AMRSED based on secure inner product computation, andthen give two significantly improved AMRSED schemes to achieve various stringent confidentiality requirements intwo different threat models. To improve search experience of the data search service, further extend the two schemes tosupport more search semantics. Extensive performance evaluations have shown that the proposed scheme can achievebetter efficiency in terms of the functionality and computation overhead compared with existing ones. For the futurework, to investigate on the authentication and access control issues in searchable encryption technique.

International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE), ISSN(PRINT): 2320-9798, ISSN (Online): 2320–9801

Authentication and Access Control for Multi Keyword Ranked Search over Encrypted Data-AMRSED

Collaborative clustering increases the researcher’s interest since from last decade for sufficient recommender systems. Thereare different methods presented for CF which is based on two different categories such as user based CF and item based CF. in userbased CF, main task is to find out set of users who have similar favor patterns to a given user and recommend to the user those itemsthat other users in the same set like. In item based CF method, main task is to provide a user with the recommendation on an item basedon the item with high correlations. But exiting methods are having limitations like data sparsity, recommendation accuracy andprediction error. Here, we have proposed novel efficient Expectation Maximization (EM) Clustering and typicality-based collaborativefiltering recommendation (EmTyCo). EmTyCo method is based on existing Tyco method by using efficient Expectation MaximizationAlgorithm for clustering purpose