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;

&nbsp;

MIT World Peace University

Understanding the user and his interests is one of the major research areas towards understanding the web today. Many systems and approaches have been proposed in literature, to try and get information about the user's interests by user profiling. Various interest indicators have also been proposed in literature that helped in giving the relevant information about the user and his interests. Having the maximum number of interest indicators and ordering them according to their preferences will make a difference in providing the more relevant information to the user. In this paper we give the relative ordering of various user interest indicators that will help us to build the user profile implicitly which will help in recommending relevant information to the user with high accuracy and confidence. © 2015 IEEE.

World University root node

School of Computer Engineering & Technology

Engineering and Technology

2015 International Conference on Pervasive Computing (ICPC)

Capturing, understanding and interpreting user interactions with the browser as implicit interest indicators

Due to advancement in mobile device technology, mobile devices are becoming an inevitable part of human lives. In context of running massive applications on mobile devices, users can't utilize the potential of mobile devices in an efficient manner since mobile devices are constrained by processing power, memory requirements and battery capacity. To alleviate this resource scarcity problem of the mobile devices, mobile cloud computing is the most promising solution which combines the technologies from both the mobile computing and the cloud computing. The execution of heavy application on mobile devices is augmented by powerful and resource-abundant cloud servers. This is achieved by partitioning an application into tasks such that the computational intensive tasks are offloaded to cloud and after executing task on cloud, results are sent back to mobile device, referred to as computation offloading. In this paper, we have put forth a scalable, fault-tolerant framework for dynamically and optimally partition the application using our proposed genetic algorithm. © Springer International Publishing Switzerland 2014.

Advanced Computing, Networking and Informatics-Volume 2

AFMEACI: a framework for Mobile execution augmentation using cloud infrastructure

In recent years, there has been a tremendous growth in the web and its usage, so much so that today many users find it difficult to get information that is relevant to them. This implies that there is a need to prioritize and try to get the information which the user is interested in. The behavior of the user is dynamic which makes it difficult to track his current interests and changes in his interests. If the user's interests are asked explicitly, most users tend to either ignore giving information or fill in wrong/incomplete information. Implicit user interested identification thus becomes imperative, which will not hamper his day-to-day web usage but the system learns about the users interests automatically, unobtrusively. This paper summarizes work done in the area of identifying user's interests implicitly through the actions performed by the user through his browser, while using the web. It also proposes measures to improve the efficiency of such systems by using a combinatorial approach.

Proceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies

User implicit interest indicators learned from the browser on the client side

With the increase of ubiquitous data all over the internet, intelligent classroom systems that integrate traditional learning techniques with modern e-learning tools have become quite popular and necessary today. Although a substantial amount of work has been done in the field of e-learning, specifically in automation of objective question and answer evaluation, personalized learning, adaptive evaluation systems, the field of qualitative analysis of a student's subjective paragraph answers remains unexplored to a large extent. The traditional board, chalk, talk based classroom scenario involves a teacher setting question papers based on the concepts taught, checks the answers written by students manually and thus evaluates the students' performance. However, setting question papers remains a time consuming process with the teacher having to bother about question quality, level of difficulty and redundancy. In addition the process of manually correcting students' answers is a cumbersome and tedious task. In this paper, we put forth the design, analysis and implementation details along with some preliminary results to build a system that integrates all the above mentioned tasks with minimal teacher involvement that not only automates the traditional classroom scenario but also overcomes its inherent shortcomings and fallacies. © 2013 IEEE.

2013 6th International Conference on Emerging Trends in Engineering and Technology

Intelligent Classroom System for Qualitative Analysis of Students' Conceptual Understanding

Personalization is omnipresent everywhere in today's modern world applications. It is primarily employed to improve user experience by adapting and learning from the patterns and information extracted from the user. There are various methods of making a system learn from the user behaviour. This paper gives a review of some of the techniques used for user profiling and personalisation systems. The paper puts forth the characteristics and advantages of user profiling and why it is so essential in today's world, with specific reference to internet usage. It also explains which factors of the usage should be taken into consideration and the importance of these factors in user profiling. The paper elaborates on the studied techniques with respect to internet usage because of its ever-growing nature, complexity in learning from the vast source, adapting to changes in usage patterns and the ultimate objective of providing a better user experience while being on-line. The commonality and differences in various proposed techniques are also summarised and highlighted. © 2013 IEEE.

Sixth International Conference on Emerging Trends in Engineering and Technolog

Comparison of Personalisation Systems

Collaborative caching and browsing allow a group of users to utilize internet bandwidth in an optimal manner by minimizing outgoing requests and allowing cooperation during web research activities. LetSurf allows a group of clients to retrieve cached materials from within the group while collaborating through a user interface to access items indicative of group behavior, such as the most recent links visited by peer users and the most popular links visited. An implementation of the system is presented and evaluated in terms of latency in loading a sample set of web elements. © 2011 IEEE.

2011 Fourth International Conference on Emerging Trends in Engineering &amp; Technology

LetSurf-Implementing Collaborative Surfing

Since the inception of the web searching technology, people have been searching for almost everything and anything on the internet. The ever-increasing dependency of users on these search engines and the dynamic nature of the World Wide Web has reduced the accuracy of the search results and increased the search time of an individual. Today, more than ever before, there is a need for search engines to be relevant and precise to the user's needs and to be able to make decisions about what the user wants to search, and should be able to suggest him similar or related topics of his interest. This increasing need of the search engine to become a decision engine [1] (term coined by Stefan Weitz [2]) brought to fore various creative technological ideas like Tag clouds and AutoComplete [3]. For a better and more relevant search experience, it is crucial that we study the present search behavior of users and its corresponding response by the search engine. This work contemplates the nature of searches made and how they evolve from time to time. In this paper we examine and construe data from various angles and then provide our suggestions and conclusions for a better, more personalized and relevant search. © 2010 IEEE.

2010 3rd International Conference on Emerging Trends in Engineering and Technology

User web search behaviour

The World Wide Web is growing at a rate of about a million pages per day, making it tougher for search engines to extract relevant information for its users. Earlier Search Engines used simple indexing techniques to search for keywords in websites and gave more weightage to pages with higher frequency of keyword occurrences. This technique was easy to trick by using meta-tags liberally, claiming that their page used popular search terms, thereby, made meta-tags useless for search engines. Another technique widely used was to repeatedly use popular search terms in invisible text (white text on a white background) to fool engines. These fallacies called for a set of algorithms which would sort the results using an unbiased parameter. The currently employed Link Analysis Algorithms make use of the structure present in 'hyperlinks', sorted and displayed depending on a 'popularity index' decided to pages linking to it. In this work, we have analyzed the mathematics behind these 'link analysis algorithms' and their effective use in ecommerce applications where they could be used for displaying 'personalized information'. © 2008 IEEE.

2008 First International Conference on Emerging Trends in Engineering and Technology

Web Search Personalization by User Profiling

A system for re-ordering the search results to provide to the user results of more relevance to him with the help of his profile. This concept is called Personalization. These profiles can be created by collecting data explicitly (which may be input directly by the user in the form of recommendation, comment or vote) as well as implicitly (based on the user's browsing patterns). This data can be monitored locally (at the client side) or at the server level or some hybrid approach can be adopted. The open recommendation system facilitates the degree to which personalization of results is needed by the user. The system design also includes measures to maintain the privacy and security of the users by encrypting their profile information to counter the threats to the same. The system has been designed as an open ended one and has a window for more ideas/designs to be incorporated some of which have been mentioned in the future works. © 2008 IEEE.

Personalization of Web search results based on user profiling

The paper introduces a new control system that's fast, accurate and intelligent. Unlike traditional techniques used till date, this system works in the optical domain i.e. it is run on OPTICAL COMPUTERS which are fast than the present day computers. The control system is a merger of Optical Computers, Fuzzy Logic, &amp; Neural Networks, Possible application of Genetic Algorithms are also proposed which are very good at finding an optimal solution. This paper deals with the advantages of optical computing, need of fuzzy logic &amp; essential merger with neural nets.