Naeem Ahmed Mahoto

Comparative Analysis of VANET Routing Protocols: On Road Side Unit Placement Strategies

Vehicular ad hoc network (VANET) is a communication paradigm where vehicles can communicate directly with other vehicles or via intermediate fixed architectures called as road side units (RSU). Generally, the density of vehicles vary from very dense to shallow depending on various factors and different scenarios of the roads. In cities the number of cars is high and on highways and specifically in rural areas the density of vehicles varies. Therefore, direct vehicle to vehicle communication faces many problems in communication due to outages. In such scenarios the intermediate infrastructure needs to play an important role. Various routing protocols are implemented for VANETs with variety of design goals. In this paper we investigate the performance of proactive and reactive routing protocols, namely destination sequence distance vector (DSDV) and ad-hoc on demand distance vector (AODV) routing protocol, for various placement strategies of RSU based relays and in city scenario. The simulations were carried on NS2 which suggest that in various settings AODV perform better than DSDV routing protocol.

MeTA: Characterization of Medical Treatments at Different Abstraction Levels

Physicians and health care organizations always collect large amounts of data during patient care. These large and high-dimensional datasets are usually characterized by an inherent sparseness. Hence, analyzing these datasets to figure out interesting and hidden knowledge is a challenging task. This article proposes a new data mining framework based on generalized association rules to discover multiple-level correlations among patient data. Specifically, correlations among prescribed examinations, drugs, and patient profiles are discovered and analyzed at different abstraction levels. The rule extraction process is driven by a taxonomy to generalize examinations and drugs into their corresponding categories. To ease the manual inspection of the result, a worthwhile subset of rules (i.e., nonredundant generalized rules) is considered. Furthermore, rules are classified according to the involved data features (medical treatments or patient profiles) and then explored in a top-down fashion: from the small subset of high-level rules, a drill-down is performed to target more specific rules. The experiments, performed on a real diabetic patient dataset, demonstrate the effectiveness of the proposed approach in discovering interesting rule groups at different abstraction levels.

Association Rule Mining in Social Network Data

The use of social networks has significantly altered the way of life of online community since last decade. The user-generated contents help to investigate various aspects of the online communities. This paper presents an approach of extracting associations between contents and contextual features of social network data. The aim is to discover the hidden correlations among the contents posted on social networking website, and detect trends of online users. The proposed approach uses association rule mining technique to uncover correlations and build taxonomy based on their corresponding relationships to deeply analyse the social network data contents. The obtained results show the efficiency of the proposed framework in mining association rules and analysing behaviours and trends of online users.