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

Due to ever increasing size of datasets with an extremely high number of attributes, mining of datasets turns out to be a universal scenario in various fields such as, industry, social and scientific areas. In order to search with very large databases, clustering is an important tasks required in various applications. Moreover, clustering algorithms that provide better scaling to very big data are significant and useful. When the size of the data become too big, it is unfeasible to store it in a single computer memory and also it consume more time for entire kernel matrix computation. In order to alleviate this problem, we propose a multiple kernel and a swarm-based MapReduce framework for big data clustering to improve the clustering accuracy. To map the input data, we implement a new clustering algorithm using tangential kernel and spherical kernel, which is used for calculating the relevant cluster centroid. Then, particle swarm based optimization (PSO) is utilized in reducer function, where the fitness value is calculated using Davis-Bouldin (DB) index. The proposed multi kernel swarm based MapReduce framework is experimented with skin and localization data sets. It can be concluded that, the proposed MKSMRF achieves higher clustering accuracy of 81% and 85% for localization and skin dataset respectively, as compared with other clustering algorithms.

Engineering and Technology

International Review on Computers and Software (I.RE.CO.S.), Vol. 11, N. 11

MKS-MRF: a Multiple Kernel and a Swarm-Based Map ReduceFramework for Big Data Clustering

Parallel computing deals with simultaneous computation of problems to speed up the total time required for serial computation. Many problems involving huge number of computations can be sub divided into smaller ones each performing some computation. However to obtain smaller fragments of computation requires dependency analysis among the sub-parts. Manually analyzing each computation for dependencies is a difficult task for developer. In this paper, we are proposing graph based dependency analysis approach for parallelizing Java programs. A program consists of sequence of statements, dependency analysis is done to check the ordering of statements in a program and also to identify various programming constructs which are dependent on each other. High level language program can be analyzed on the basis of data flow between variables and communication among functions. This study provides a method to parallelize input Java code based on automated generation of dependence graph. Dependence graph depicts the connectivity between the programming constructs i.e variables and functions. When these dependencies are resolved then it is possible to split program into independent modules which can be executed in parallel with each other for achieving high performance. © 2016 ACM.

World University root node

ACM International Conference Proceeding Series

Static structural dependency analysis for parallelization of Java programs

Businesses are increasingly using web services to expose and exchange data over the web. Last few years have viewed tremendous growth in web service as a technology for inter application logic sharing and data retrieval. As web services have been able to provide simple solutions of very generic business problems it has got much higher acceptance and presence over the internet. Today internet is flooded with web services which give rise to problem of selection of web service that will best fit the business use case. To work through the problem of overflow of web services over internet a very efficient web service selection mechanism is need of the hour. In this paper we proposed a novel approach for web service recommendation system. © 2016 ACM.

A novel approach for web service recommendation

As the wireless sensor network (WSN) is consider, it is the huge network of low-cost micro sensors deploy in the environment. The main challenge in the design of WSN is to improve the lifetime of the network. Various activities are done in the sensor node which consumes lots of energy due to which there is a need to save energy of node. The battery of the node is difficult to recharge in wireless scenarios, due to this problem the energy efficient routing is used to solve these problems. To achieve the aim, we need not only to minimize total energy consumption along with this balance therouting loading WSN. Researchers have proposed many protocols such as LEACH, HEED, PEGASIS, TBC and PEDAP. We are proposing a Fuzzy rule based General Self-Organized Tree-Based Energy-Balance routing protocol (FGSTEB) which is based on the GSTEB, in this work we builds a routing tree for data transmission by selecting the appropriate routing path and relay node using fuzzy rules, fuzzy rules finds relaying node depending on their residual energy and the load on the node. Due to the dynamic nature of the FGSTEB protocol and performs the simulation according to different parameter in consideration. The main goal of the work is to improve the performance of GSTEB protocols by balancing energy consumption which prolonging the lifetime of WSN. © 2015 IEEE.

International Conference on Energy Systems and Applications, ICESA 2015

Energy efficiency in wireless sensor network using Fuzzy rule and tree based routing protocol

Owing to the rising popularity of social networking sites and chat-based applications, visual sentiment clues such as emoticons are increasingly being used in blogs, tweets, games, and product reviews. The existing sentiment analysis tools mainly focus on predicting the polarity based on textual content, and displaying the results in the form of graphs or charts. In this paper, we propose a system to account for emoticons and exclamation marks along with words while performing sentiment analysis of the input text. The output of this analysis is represented on a unique figure, which we define as an ‘emoticon-graph’. An online survey was conducted to collect product and news reviews to analyze the sentiment and also to evaluate the acceptance of the ‘emoticon-graph’. The findings of this survey indicate that dynamically plotted emoticon-graphs could play a major role in simplifying the results of polarity determination methods. © Springer Science+Business Media Singapore 2016.

Advances in Intelligent Systems and Computing

A novel approach for polarity determination using emoticons: Emoticon-graph

Every year the number of movie produced and released surpass the previous year's count and so do the total box office collections. So in this quality centric industry, it becomes imperative that the movie succeeds both in terms of box office collections and critical reviews and also renders profit. Due to advent of predictive analytics and big data generated through various social interactions, models to predict accurately the total gross of a movie can be devised, which eventually help the movie studio by giving constructive feedback both in pre-production and post-production phase. So the availability of this data gathered from various social platforms like IMDb, YouTube and Wikipedia can help to gauge the society's reaction and response towards a particular movie. It can also foretell a society's anticipation towards a particular movie. In this paper, we have built predictive models by establishing links between classical features, social media features and the overall success of the movie which includes total box office collection and the critics rating or review. The results show that the prediction model built using integration of classical as well as social factors can achieve higher accuracy rate. © 2016 IEEE.

2016 International Conference on Internet of Things and Applications, IOTA 2016

Relationships between classical factors, social factors and box office collections

At present Brain Computer Interface (BCI) technology plays an important role in the field of biomedical. BCI can be used to identify human stress based on various emotion states - happy, sad, disgust, fear. Various techniques such as Neural networks (ANN), statistical methods, autoregressive model (AM), mixture of densities approach, independent component analysis (ICA), time-frequency analysis (TFA), bayes quadratic, hidden markov model (HMM) and linear discriminate analysis (LDA) have been used for EEG signal analysis. There are some issues which are being focused on while extracting features are -handling irrelevant and redundant features require more computation for decomposition of EEG signals. And the paper is considering following issues while carrying out classification - inability in processing complicated set of data, low performance when training data set is huge. This paper focuses on addressing above mentioned issues and appropriately analyzes various EEG signals which would in turn help us to improve the process of feature extraction and improve the accuracy in classification. In addition to acknowledging above problems, this paper proposes a framework which would be helpful in identifying human stress level and as a result, differentiate a normal or stressed person/subject.

Research Journal of Pharmaceutical, Biological and Chemical Sciences

Literature review of human stress analysis using EEG signal

International Research Journal of Engineering and Technology (IRJET)

A survey of HTTP botnet detection

A Survey of Comparative Analysis of Secure Passwords using CaRP by Different Techniques

In this paper, Motion Estimation (ME) and Motion Compensation (MC) techniques are used for video compression which eliminates the temporal redundancy of video sequence. For motion estimation, Block Matching (BM) algorithm has been used among the various motion estimation algorithms because of its simplicity and efficiency. In this paper, the performance of block matching algorithm such as Four Step Search (4SS) has been improved using Block Transform Scaling (BTS). In BTS all the varying nature frames of video transform into uniform frames using adaptive filter, which fills the pixel information where pixel value is missing within the frame. The proposed work mainly focuses on the compression of videos which have medical information in it. At the end of this paper the result analysis has been done using the parameters such as Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Computation Time (CT) and Compression Ratio (CR). © 2015 IEEE.