MapReduce neural network framework for efficient content based image retrieval from large datasets in the cloud
- Authors: Venkatraman, Sitalakshmi , Kulkarni, Siddhivinayak
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: Recently, content based image retrieval (CBIR) has gained active research focus due to wide applications such as crime prevention, medicine, historical research and digital libraries. With digital explosion, image collections in databases in distributed locations over the Internet pose a challenge to retrieve images that are relevant to user queries efficiently and accurately. It becomes increasingly important to develop new CBIR techniques that are effective and scalable for real-time processing of very large image collections. To address this, the paper proposes a novel MapReduce neural network framework for CBIR from large data collection in a cloud environment. We adopt natural language queries that use a fuzzy approach to classify the colour images based on their content and apply Map and Reduce functions that can operate in cloud clusters for arriving at accurate results in real-time. Preliminary experimental results for classifying and retrieving images from large data sets were quite convincing to carry out further experimental evaluations. © 2012 IEEE.
- Description: 2003010699
A biometric based authentication and encryption Framework for Sensor Health Data in Cloud
- Authors: Sharma, Surender , Balasubramanian, Venki
- Date: 2014
- Type: Text , Conference proceedings
- Full Text:
- Description: Use of remote healthcare monitoring application (HMA) can not only enable healthcare seeker to live a normal life while receiving treatment but also prevent critical healthcare situation through early intervention. For this to happen, the HMA have to provide continuous monitoring through sensors attached to the patient's body or in close proximity to the patient. Owing to elasticity nature of the cloud, recently, the implementation of HMA in cloud is of intense research. Although, cloud-based implementation provides scalability for implementation, the health data of patient is super-sensitive and requires high level of privacy and security for cloud-based shared storage. In addition, protection of real-time arrival of large volume of sensor data from continuous monitoring of patient poses bigger challenge. In this work, we propose a self-protective security framework for our cloud-based HMA. Our framework enable the sensor data in the cloud from (1) unauthorized access and (2) self-protect the data in case of breached access using biometrics. The framework is detailed in the paper using mathematical formulation and algorithms. © 2014 IEEE.