Cloud Computing is an emerging technique in recent years that provides computing as a services. In order to maximize resources utilization, many scheduling algorithms were analyzed and implemented. Job scheduling using Berger model is one of the algorithm for scheduling jobs. The combination of Berger model and Neural Network would overcome the disadvantage of Berger Model i.e., incompletion of task when tasks-resources match is not achieved. In this work, the submitted jobs are classified based on different parameters like bandwidth, memory, Completion time and Resources Utilization. The classified user tasks are passed to the neural network. Neural network consists of input layer, hidden layer and output layer. With the help of hidden layer, the jobs are matched with the resources by adjusting weight. The performance of the system has been improved by means of efficient use of bandwidth, reducing a completion time which in turn improves resources utilization. CloudSim, a simulation tool has been used to simulate and the results shows reduced completion time and increased performance of the system.

Hepatitis B is a potentially life-threatening liver infection caused by the hepatitis B virus. The virus interferes with the function of the liver while replicating in hepatocytes. It is a major global health problem and the most serious type of viral hepatitis. Chronic liver disease is caused by viral hepatitis and putting people at high risk of death from cirrhosis of the liver and liver cancer. Medical information available is extensive and which is utilized by the clinical specialists. The ranging of information is from details of clinical symptoms to various types of biochemical data. Information provided by each data is evaluated and assigned to a particular pathology during the diagnostic process. Artificial intelligence methods especially computer aided diagnosis and artificial neural networks can be employed to streamline the diagnostic process. These adaptive learning algorithms can handle diverse types of medical data and integrate them into categorized outputs. Artificial neural networks are finding many uses in the medical diagnosis application. In this paper we have proposed a Generalized Regression Neural Network (GRNN) based expert system for the diagnosis of the hepatitis B virus disease. The system classifies each patient into infected and non-infected. If infected then how severe it is in terms of intensity rate.

This main aim of this project to provide voice-based navigation system for blind people using voice recognition module and it is intended to provide overall measures object detection and real time assistance via Global Positioning System (GPS) and ultrasonic sensors. This project aims at the development of an Electronic Travelling Aid (ETA) kit to help the blind people to find obstacle free path. This ETA is fixed to the stick of the blind people. When the object is detected near to the blinds stick it alerts them with the help of vibratory circuit. The blind person will give the destination’s name as the input to voice recognition module. GPS module continuously receives the latitude and longitude of the current location. GPS compares it with the destination’s latitude and longitude. The blind person receives the pronounced directions which he needs to follow to reach his destination.

No abstract

No abstract