P. Saravanan

Bio: P. Saravanan is an academic researcher from Shanmugha Arts, Science, Technology & Research Academy. The author has contributed to research in topics: Recommender system & Encryption. The author has an hindex of 6, co-authored 19 publications receiving 149 citations.

Dynamic particle swarm optimization for personalized recommender system based on electroencephalography feedback

Abstract: Rapid growth of recommender systems (RSs) had proved its potential in the generation of personalized recommendations in various application domains. Generally, RSs learn the user's preferences and interests to suggest relevant items to the users. RSs are widely employed in various domains such as movies, e-commerce, travel, etc. Due to rapid growth in travel applications, Travel Recommender Systems (TRSs) had received a significant attention from researchers. Though existing TRS help users as digital support assistants in the travel, still the TRSs faces huge barriers in understanding user interests based on user's current emotional context. In this paper, to generate effective personalized Point of Interest (POI) recommendations, we present a Dynamic Particle Swarm Optimization and Hierarchy Induced K-Means (DPSOHiK) approach for the better POI clustering through utilizing electroencephalography (EEG) feedback. The DPSOHiK approach, with its capabilities to adapt the changing attributes helps in the POI clustering process. The clustered POIs are utilized in the recommendation process and based on the user's personal preferences the POIs are ranked to meet the requirements of the user. We have experimentally evaluated our proposed recommendation approach to demonstrate the recommendation potential and compared the obtained results with the baseline approaches. The experimental results depict the importance of EEG feedback in the enhancement of recommendation accuracy and provide helpful insights to the researchers to utilize EEG in the RSs research and development.

Meta-data based secret image sharing application for different sized biomedical images

Abstract: Usually in image sharing schemes, shares are generated first for a given secret image and then embedded into cover images to produce stego images. These two steps are done sequentially. There exist some relationship in the first step, the size of the secret image and size of the shares which are derived from them. In the proposed method, these two steps are done concurrently. A cover image is chosen and according to its embedding capacity, share is generated and subsequently embedded into chosen cover to produce the stego image. This process is repeated till all the image portions are embedded. While generating share, meta-data (i.e.) header is created for each shares and appended to shares before being embedded. At the destination end, shares are extracted from each stego images and are reassembled into a single original secret image according to the meta-data present in each share. Methods available in the literature embeds uniform sized secret image into cover images of uniform sizes. Using proposed method different sized secret images have been embedded into cover images of varying sizes.

Data mining approach for subscription-fraud detection in telecommunication sector

Abstract: This paper implements a probability based method for fraud detection in telecommunication sector. We used Naïve-Bayesian classification to calculate the probability and an adapted version of KL-divergence to identify the fraudulent customers on the basis of subscription. Each user’s data corresponds to one record in the database. Since, the data involves continuous numerical values, the NaïveBayesian classification for continuous values is used. This methodology overcomes the problem of existing system, which classifies the best customer as fraudulent customers, as it works on a threshold based method.

A Survey on Internet of Things and Cloud Computing for Healthcare

Abstract: The fast development of the Internet of Things (IoT) technology in recent years has supported connections of numerous smart things along with sensors and established seamless data exchange between them, so it leads to a stringy requirement for data analysis and data storage platform such as cloud computing and fog computing. Healthcare is one of the application domains in IoT that draws enormous interest from industry, the research community, and the public sector. The development of IoT and cloud computing is improving patient safety, staff satisfaction, and operational efficiency in the medical industry. This survey is conducted to analyze the latest IoT components, applications, and market trends of IoT in healthcare, as well as study current development in IoT and cloud computing-based healthcare applications since 2015. We also consider how promising technologies such as cloud computing, ambient assisted living, big data, and wearables are being applied in the healthcare industry and discover various IoT, e-health regulations and policies worldwide to determine how they assist the sustainable development of IoT and cloud computing in the healthcare industry. Moreover, an in-depth review of IoT privacy and security issues, including potential threats, attack types, and security setups from a healthcare viewpoint is conducted. Finally, this paper analyzes previous well-known security models to deal with security risks and provides trends, highlighted opportunities, and challenges for the IoT-based healthcare future development.