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

We are living in the era where the video contents are largely available on the internet and hard disk drives. Since the duration of videos ranges from few minutes to many hours, the progression of a system for decisive content classification, browsing, indexing, retrieval and storage become essential. Also, with the advancement of the digital video technologies, huge chunks of video data are producing every day. For processing these data, video summarization plays an important role, which is a process of generating a synopsis of the video. In this paper, we present application scenarios of video summarization and some of the techniques which fit in a particular application. With fast evolution of video technology, nowadays many new multimedia applications are available. We categorize different applications of video summarization and methods for generating the summary of a video. In this paper, we introduce different types of videos and need of summarization for each type of video. © 2018 IEEE.

World University root node

School of Computer Engineering & Technology

Engineering and Technology

Proceedings of the 2nd International Conference on Electronics, Communication and Aerospace Technology, ICECA 2018

A Study on Application Scenario of Video Summarization

We proposed a system to provide a recommendation system which will generate the user interested colleges list. This will be done by asking few questions related to the college like-college infrastructure, campus life, placement, sports and cultural activities. Information seeker will give ratings to the questions according to his interest rate. The list of college will be stored in the dataset for calculating the accuracy and precision of the system. Also the students will be giving feedback to the system for their performance. © 2017 IEEE.

International Conference on Advances in Computing, Communication and Control 2017, ICAC3 2017

Analysing recommendation of colleges for students using data mining techniques

Currently Internet usage has increased a lot due to bandwidth availaility and technology advancements. Internet is widely used for knowledge sharing, online review of products etc. Many open forums, blogs are used for this purpose. Since many users are contributing their opinions towards any query submitted by information seeker, there is a possibility of confusion. Often opinions contradict with each other creating confusion in information seeker's mind. In these cases role of Opinion Leader(s) is very prominent. Opinion Leader is a person who has knowledge in the particular field, who's opinion makes difference and who can influence other's opinions. Identification of a person who has great experiences and/or knowledge, in a particular domain, is very helpful and useful in decision making, product marketing etc. This paper presents an approach for identification of Opinion Leader(s) using modified SPEAR (Spamming Resistant Expertise Analysis and Ranking) algorithm. The expertise of user is found out on different topics. Modified SPEAR algorithm effectively identifies Opinion Leader(s) by making use of additional influence measures in the form of credit score functions. It also analyses these measures and studies their effects while ranking the Opinion Leader(s) effectively. © 2016 IEEE.

International Conference on Computing, Analytics and Security Trends, CAST 2016

Identification of topic-specific Opinion Leader using SPEAR algorithm in Online Knowledge communities

Currently in India college ranking is the issue that is discussed a lot. In this paper, a common forum where students can get information about various engineering colleges and their ranking. There are many websites that give information about various engineering colleges but when it comes to students, for them it is important to know certain insights to parameters like faculty, campus life, hostel facility, placement etc. Students from small cities and villages have less exposure to various Colleges which are funded by state and central government for studies, Research and Innovation. If these students will have sufficient exposure then the young talent will be properly channelized as per their interest. In this we have developed a recommender system that understands information seeker need and accordingly generates recommendations for him/her through simple interface, which provides information to the students and advisors to improve the choice of courses. In our methodology we have opted for hybrid approach which combines advantages of Collaborative filtering algorithm and Content based filtering algorithm to improve our recommendations. The system consists of an interactive User Profiling [9] process in order to construct profile of the User. As the sample datasets that are used for experiments are large and also contain more number of feature sets, it is essential to understand dataset beforehand. Recommending items based on the users interest requires processing of these large datasets, thus if this information is properly profiled then the task of recommending would get a lot easier. Also when results are shown to the user, big challenge is how well data can be ranked so that user satisfaction is guaranteed. © 2017 IEEE.

Proceeding - IEEE International Conference on Computing, Communication and Automation, ICCCA 2017

A user-friendly college recommending system using user-profiling and matrix factorization technique

Recommendation systems aim at recommending relevant items to the users of the system. Recommendation Systems provide efficient recommendations based on algorithms used for classification and ranking. There exist various ways by which classification can be achieved in a supervised or unsupervised manner. Since the sample datasets that are used for experiments are large and also contain more number of feature sets, it is essential to understand dataset beforehand. Also when results are shown to the user, big challenge is how well data can be ranked so that user satisfaction is guaranteed. When data sets are large, some ranking algorithms perform poorly in terms of computation and storage. Thus, these kinds of algorithms are quite expensive. We aim at developing classification and ranking algorithm which will reduce computational cost and dimensionality of data without affecting the diversity of the feature set. Dimensionality of data can be handled by SVM (Support Vector Machine). AUC (Area under the Curve) andWARP (Weighted Approximately Ranked Pairwise) algorithms are efficient for ranking of the items which are of user interest. © 2015 IEEE.

12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015

Performance analysis of classification and ranking techniques

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

Recommendation Systems (RSs) are becoming tools of choice to select the online information relevant to a given user. Collaborative Filtering (CF) is the most popular approach to build Recommendation System and has been successfully employed in many applications. Collaborative Filtering algorithms are much explored technique in the field of Data Mining and Information Retrieval. In CF, past user behavior are analyzed in order to establish connections between users and items to recommend an item to a user based on opinions of other users. Those customers, who had similar likings in the past, will have similar likings in the future. In the past decades due to the rapid growth of Internet usage, vast amount of data is generated and it has becomea challenge for CF algorithms. So, CF faces issues with sparsity of rating matrix and growing nature of data. These challenges are well taken care of by Matrix Factorization (MF). In this paper we are going to discuss different Matrix Factorization models such as Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Probabilistic Matrix Factorization (PMF). This paper attempts to present a comprehensive survey of MF model like SVD to address the challenges of CF algorithms, which can be served as a roadmap for research and practice in this area. © 2015 The Authors.

Matrix Factorization model in Collaborative Filtering algorithms: A survey

Identification of opinion leader is very important in this world of internet because with the identified opinion leaders in any application area such as Knowledge related sites, followers or other individuals can get valuable information more efficiently through direct communication with opinion leader. Internet i.e. WWW (World Wide Web) is the huge and very popular way of information broadcasting and communication. This huge www has so many web structures and within one structure there would be millions of web resources (contents, links) may exist. There are large numbers of webpages on the web which are linked to each other through hyperlinks. So, graph based techniques can be used to identify opinion leader i.e. techniques for ranking the results to provide the "best" results first. Different algorithms are there which are used for link analysis i.e. for ranking the web pages like PageRank (PR), Weighted PageRank (WPR), Hyperlink-Induced Topic Search (HITS), Spamming Resistant Expertise Analysis and Ranking (SPEAR) etc. This paper is focused on the study of different ranking techniques. Further this paper shows advantages, limitations and comparison of these techniques.

International Journal of Computer Applications

A Survey of various Web Page Ranking Algorithms

E-Commerce has already entered into the Indian market for online shopping. People are more inclined towards online shopping which has changed the complete market scenario. There are several online shopping portals offered by organizations such as, Amazon, Flipkart, Snapdeal, Junglee, Jabong, and others, which are enjoying their online market share. As the number of online buyers and traders are increasing, effective business techniques need to be adopted to handle the large amount of data generated every day. Recommendation Systems play an important role in filtering this data and providing adequate information to the users. Various techniques like Collaborative Filtering, Content-based, and Demographic have been adopted for recommendation but there are several drawbacks causing these techniques to fail in providing effective recommendations. Therefore, it is necessary to identify more distinguishing features for optimizing these techniques. This can be achieved through utilizing the strengths of various techniques in a hybrid manner. This paper describes an effective hybrid technique for book recommendation with the use of Ontology for user profiling to increase system efficiency. © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.