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

Classification of imbalance data is the major challenge to the community these days. Machine learning algorithms can evolve a one-sided classifier when data is imbalance. The vital challenge in imbalance dataset problem is that sometimes the minority (tiny) classes are more useful, but standard classifiers tend to be biased toward the majority (huge) classes and ignore the tiny ones. In this paper we compared the existing methods to handle imbalance dataset and provide a new hybrid approach which will improve the accuracy of classifier on imbalanced data. © 2016 IEEE.

World University root node

Engineering and Technology

International Conference on Communication and Signal Processing, ICCSP 2016

A new approach for handling imbalanced dataset using ANN and genetic algorithm

International Journal of Science, Engineering and Management

Pixel N-grams: Size, Location and Resolution Invariance for Shape Classification

A survey of cloud supporter: a load balancing framework in cloud using smartphone

Annual Fedderation University Conference

Pixel N-grams for Mammograms Classification

User Profiling is the process of Extracting, Integrating and Identifying the keyword based information to generate a structured Profile and then visualizing the knowledge out of these findings. User profiling helps personalizing a system to work according to user. Therefore user profiling or personalization is one of the major concepts used for accessing the user relevant information, which can be used to solve the difficult problems of recommender system like classification and ranking of items in accordance with an individual's interest. In this paper we focus on the problem of user profiling in which we aim at finding, extracting and integrating keyword based information from various web sources to generate a structured profile. Further we do some experiments on the profiled information to generate knowledge out of it. © 2016 The Authors.

Fulltext

Procedia Computer Science

User Profiling for University Recommender System Using Automatic Information Retrieval

Collaborative Filtering (CF) algorithms are most commonly used prediction technique in field of Recommender Systems (RS) for Information Filtering. It makes use of single criteria ratings that user have assigned to items which plays an important role in e-commerce to assist users in choosing items of their interest. For complex and massive dataset, Multi-Criteria Collaborative Filtering (MC-CF) frequently give better performance as well as accurate and high quality recommendations for users considering multiple aspects of items. CF algorithms need to be continuously updated because of a constant increase in the quantity of information, ways of access to that information, scalability and sparseness in rating matrix. Dimensionality Reduction techniques like: Matrix Factorization and Tensor Factorization techniques have proved to be a quite promising solution to the problem of designing efficient CF algorithm in the Big Data Era. This work aims at offering University Recommendation System, which combines MC-CF and Dimensionality Reduction techniques to provide high quality University/College recommendation to Students. The proposed solution not only reduces the computation cost but also increases the prediction accuracy and efficiency of the MC-CF algorithms implemented using the Apache Mahout framework. © 2015 IEEE.

Proceedings - 2015 IEEE International Symposium on Nanoelectronic and Information Systems, iNIS 2015

An Approach to a University Recommendation by Multi-criteria Collaborative Filtering and Dimensionality Reduction Techniques

Now a day, the mass events like political rallies, musical concerts, sports matches, school gathering and pilgrimage become an issue of the public security. In the past the research is based on the detection of the human behaviour and for finding the hazardous location. Crowd is a collection or gathering of many people in the same area at the same time. In such situation the density of people is high and this may causes the disaster. The proposed system will help to overcome such situation. The system detects the congestion with the help of optical flow and the standard deviation and gives warning when the congestion is detected. © 2016 ACM.

ACM International Conference Proceeding Series

A congestion detection system using optical flow and standard deviation in mass gathering

Wireless Communication and Wireless Networking is the popular research in this era. The combination of this is useful method for one step ahead to increase the life of human being on this world. The issue is to increase the growth of all this living mankind from different serious diseases, so the technology and communication is BAN (Body Area Network) through wireless is Wireless Body Area Network. We do the research on the adaptive energy efficient Medium Access Control protocol used in WBAN, so to increase the life of sensor node. Once the energy is utilized we cannot extend its energy only by replacing the battery. Proposed protocol incorporates dynamic duty cycle as well as adaptive contention window schemes. We have simulated the protocol in NS2 environment and checked the performance of data transmission in different condition may be in Normal, On-demand and Traffic which leads to consume more energy. © 2016 IEEE.

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

Adaptive energy efficient MAC protocol for increasing life of sensor nodes in Wireless Body Area Network

OpenStack is a cloud computing platform. OpenStack provides an Infrastructure as a Service (IaaS). OpenStack constitutes resources such as compute, storage and network resources. Resource allocation in cloud environment deals with assigning available resources in cost effective manner. Compute resources are allocated in the form of virtual machines (aka instances). Storage resources are allocated in the form of virtual disks (aka volumes). Network resources are allocated in the form of virtual switches, routers and subnets for instance. Resource allocation in OpenStack is carried out by nova-scheduler. However, it is unable to support providers objectives such as allocation of resources based on user privileges, preference to underlying physical infrastructure, actual resource utilizations for example, CPU, memory, storage, network bandwidth etc. An improved nova-scheduler algorithm considers not only RAM, CPU but also vCPU utilization and network bandwidth. Improved nova-scheduler is referred as metrics-weight scheduler in this paper. This paper gives performance evaluation and analysis of Filter-scheduler and Metrics-weight scheduler. © 2015 IEEE.

Proceedings - IEEE International Conference on Information Processing, ICIP 2015

Improved filter-weight algorithm for utilization-aware resource scheduling in OpenStack

There is a substantial emphasis on security design of Text based CAPTCHA by way of providing the security at pre-processing layer, segmentation layer and character recognition layer. The strength to protect the system from security threats defines the robustness of any given system and which in turn depends on number of attempts the malicious program, which does not have direct access, will need to defend it. This paper proposes a systematic approach to measure the qualitative strength of the Text based CAPTCHA depending on type of TBC, Length of TBC, Character recognition rate and its response time. Applying our systematic approach to different CAPTCHA providers and users, we found that approximately 80% CAPTCHAs are weak and susceptible to our attacks. Along with the attacks leading to weakness of text based CAPTCHA, we have provided the distinguishing features of different CAPTCHA Segmenter which play an important role in defending anti-segmentation techniques. © 2015 IEEE.