Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the coverage of the most important and influential journals to meet the needs of the global scientific community.  
ASCI Database
308-Lasani Town,
Sargodha Road,
Faisalabad, Pakistan
Fax: +92-41-8815544
Contact Via Web
Suggest a Journal
Articles by G. Hariharan
Total Records ( 2 ) for G. Hariharan
  G. Hariharan , A. Jayachandran , G. Jiji , M. Rajaram and T. Ajith Bosco Raj
  Classification of medical imagery is a difficult and challenging process due to the intricacy of the images and lack of models of the anatomy that completely captures the possible deformations in each structure. Cervical cancer is one of the major causes of death among other types of the cancers in women world wide. Proper and timely diagnosis can prevent the life to some extent. Therefore we have proposed an automated reliable system for the diagnosis of the cervical cancer using texture features and machine learning algorithm in PAP smear images, it is very beneficial to prevent cancer also increases the reliability of the diagnosis. In this research study, we have developed, multi class cervical cancer severity analysis system based on hybrid texture features and hybrid RBF kernel based support vector machine using PAP smear images. Two major contribution of the proposed system is feature extraction and feature classification. In feature extraction, multiple features are extracted using texture features and Gabor filter based orientation image. This system classifies the PAP smear cells into anyone of four different types of classes using RBF-SVM. The performance of the proposed algorithm is tested and compared to other algorithms on public image database of Herlev University Hospital, Denmark with 452 PAP smear images. The overall classification accuracy of the proposed hybrid RBF-SVM is 96.8% but the existing methods RBF and SVM produce 91.32 and 94.32%, respectively.
  R. Arangasamy , J. Sundararajan , G. Shankar and G. Hariharan
  The main aim of this study is to inject insulin for type 1 diabetes using pumping of insulin. The blood glucose level has to be monitored with the help of glucose tolerance test. For efficient method to control type 1 diabetes researcher will use embedded linear parameter varying methodology controller. In this study, there are three things researchers need to focus. The first is the sensor values read from the sensors has to be monitored and the second is the lab information (patient’s basic level of tests). Depending on the patients test details, the insulin has to be injected. For example, if the person sensor value is greater than the reference limit then he has to be provided with insulin for a longer period. So, the comparison of lab details with the patients current sensor values play a vital role in determining the insulin level for a patient. Finally, the feedback has to be obtained with the help of those comparisons and it has to be sent once again as a loop to the controller for later comparison and also for database information. Here, the controller is the key element for updating all the information about the patient and it will control all the parameters of the board. Here, researchers will use EEPROM for saving all the data on a location basis. It is capable of holding 256 bytes at a time and each location can store one byte information at a time.
Copyright   |   Desclaimer   |    Privacy Policy   |   Browsers   |   Accessibility