This paper presents an intelligent, mobile agent based framework for knowledge discovery in a distributed healthcare environment comprising multiple heterogeneous healthcare data repositories. The need for quality and demand for intelligent decision making systems in health care is very high. Tremendous forage of diseases as well increase in type of treatments and varying medicine contents have increased the complexity of Health Care.
Challenging issues such as introducing decision support Intelligent and Mobile Agent based health care service over pervasive mobile computing devices is the main focus of this research work. Issues such as system adaptability, reliability, robustness, extensibility, flexibility to be maintained has motivated our research to explore mobile agent approach for development of wireless mobile systems for tele-health and tele-medicine.
This paper addresses on the current state of decision support systems where Intelligent Mobile Agent technology can be deployed to assist decision-makers (patients, physicians, managers or policy makers) over survey about Diseases, Treatments, Patient Record Maintenance, and Medical Information Management. The proposed architecture discusses on Personal HealthCare with its functionality, algorithm and operations. The implementation and its working model can be carried out as future work of this project.
Contact Dr ARUNKUMAR THANGAVELU (proftarunkumar@gmail.com)
The learning process is a process of communication and interaction between the teacher and his students on one side and between the students and each others on the other side. Interaction of the teacher with his students has a great importance in the process of learning and education. The pattern and style of this interaction is determined by the educational situation, trends and concerns, and educational characteristics.
Classroom interaction has an importance and a big role in increasing the efficiency of the learning process and raising the achievement levels of students. Students need to learn skills and habits of study, especially at the university level. The effectiveness of learning is affected by several factors that include the prevailing patterns of interactive behavior in the classroom. These patterns are reflected in the activities of teacher and learners during the learning process. The effectiveness of learning is also influenced by the cognitive and non cognitive characteristics of teacher that help him to succeed, the characteristics of learners, teaching subject, and the teaching methods.
This paper presents a machine learning algorithm for extracting knowledge from student model. The proposed algorithm utilizes the inherent characteristic of genetic algorithm and neural network for extracting comprehensible rules from the student database. The knowledge is used for discriminating male and female levels in logical reasoning as a part of an expert system course.
