Jaypee Institute of Information Technology

About: Jaypee Institute of Information Technology is a education organization based out in Noida, Uttar Pradesh, India. It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2136 authors who have published 3435 publications receiving 31458 citations. The organization is also known as: JIIT Noida.

Distance and direction-based location aided multi-hop routing protocol for vehicular ad-hoc networks

Abstract: Vehicular ad-hoc network VANET is an important technology for future development of intelligent transportation systems. Due to the highly-dynamic nature of vehicular nodes, network topology changes very frequently which complicate the routing of data packets. A number of routing protocols have been developed by various researchers. Hop count is a key parameter in evaluating the performance of a routing protocol. Number of hops required in directional routing protocols is more in comparison to other routing protocols. For a city vehicular traffic scenario we propose an improved direction-based location aided routing D-LAR protocol that we call distance and direction-based location aided routing DD-LAR protocol. A mathematical model for DD-LAR protocol has been developed to examine its performance on path duration and hop count metric. Simulations have been done in MATLAB and from the results, it can be seen that DD-LAR routing protocol shows significant performance improvement over D-LAR and LAR in terms of reducing the hop count and maximising the path duration.

Social graph based location recommendation using users' behavior: By locating the best route and dining in best restaurant

Abstract: Study of users' behavior is increasingly becoming a topic of research because of innovations in web. Out of many research areas, check-ins on Facebook is a rather best way to connect with users' places of interest. Services such as location recommendation would definitely benefit from such research. There are times when people are stuck in situations where they are completely new to a particular place due to lack of location information and trends based recommendations, basically they don't have idea to explore a specific location. Therefore many times users don't have information to plan their trip as to fetch maximum benefits from the trip. The prime objective of this research work is to understand, analyze and suggest location and restaurants on the basis of user behavior. To achieve this, we developed an application ‘Travel Best’ which focuses on extracting check-ins from the Facebook accounts of the users and generate trends based suggestion for users to travel best. Within this, users can explore nearby trending regions of places within a city according to facebook checked-in data. To add on a cross domain flavor to the system, we also analyze the restaurants data based on ratings and reviews for the searched location according to Zomato extracted information. In our proposed approach, we map location based data to suggest location on the basis of shortest path, longest path and most traveled route and visualized our result using graph database. Further to suggest restaurants of the chosen location or chosen path, we apply opinion mining to provide relevant ratings on the basis of different categories namely food quality, service and ambiance unlike overall ratings usually provided by other applications.

Recommendation research trends: review, approaches and open issues

Abstract: Recommendation systems have been well established to reduce the problem of information overload and have become one of the most valuable tools applicable to different domains like computer science, mathematics, psychology, etc. The initial interest to write this survey is composing a concise research paper on the key motivation behind the various existing recommender systems and their techniques used in various domains. In this paper, prioritisation of recommendation keywords is presented in form of weighted keyword network along with keywords associations according to their usage in reference section literature. Consequently, this paper provides comprehensive details of various public datasets, their corresponding techniques, comparative analysis of existing recommendation approaches based on faced challenges and performance measures are examined. This study will help the researchers and academicians in quickly understanding the existing work and in planning future recommendation studies for designing a unified and coherent recommender system.