Research
An Efficient Emergency, Healthcare, and Medical Information System
Many of the current Indian medical information and emergency systems are still paper-based and stand alone systems that do not fully utilize the Internet, multimedia, wireless and real time technologies. This project work focuses on developing an integrated Emergency, Healthcare, and Medical Information System (IEHMS) that can overcome many of the problems in the current systems. The main aim of this work is to incorporate the real-time technologies with medical emergency systems. Our proposed system can offer: SMS, MMS, Phone call, Email. A prototype for the proposed system is implemented using open source tools. The system will revolutionize the delivery of emergency medical services, like ambulance, first aid etc, The call center of Emergency Management and Research Institute took up the case and identified the exact location where the patient had collapsed. When attending a call, the user check for the similar calls and search for the ambulance which is located to that area and ambulance is staffed by:  One driver  One helper  One experienced paramedic  One trained medical officer, if required For efficient medical monitoring of the patient’s condition, the institute has qualified personnel with knowledge and skills sufficient to evaluate and stabilize patients with potentially lethal or disabling conditions.
Vision-Based Deep Web Data Extraction For Web Document Clustering
The design of web information extraction systems becomes more complex and time-consuming. Detection of data region is a significant problem for information extraction from the web page. In this paper, an approach to vision-based deep web data extraction is proposed for web document clustering. The proposed approach comprises of two phases: 1) Vision-based web data extraction, and 2) web document clustering. In phase 1, the web page information is segmented into various chunks. From which, surplus noise and duplicate chunks are removed using three parameters, such as hyperlink percentage, noise score and cosine similarity. Finally, the extracted keywords are subjected to web document clustering using Fuzzy c-means clustering (FCM).
