Distributed online doctor surgery

This paper reports on redesign of the existing manual system of a Doctor Surgery, to a computerised system, which takes the advantage of the latest technologies and allows the patients to have better interaction with the system. The Doctor surgery plays a major role in human life, over the years we have seen the drastic changes in the treatment of patient in surgery, however we haven't really seen much changes on structure of the system as a whole. Many surgeries still use a manual paper based system for their transaction. The recent rapid development in web technology and growth of distributed processing seems to be only applicable for commercial business and field such as medical treatment seems to have fallen behind in the technology and as consequence, inefficient and ineffective services provided to the patients. The new prototype system has been designed using Object Oriented Methodology and implemented by using mainly JAVA (RMI, SQL, SERVLET and other Java packages) for creating the communication server and the web site. Also, for the end user interface of the database in the surgery ORACLE 7 and Developer 2000 application was used. The implementation of the system allows the patient to carry out appointment transaction (create, query, delete) and communicate with the doctor via the web site, which is connected to the oracle server in the surgery. The web site provides all the necessary details and information about the surgery and practice. The final prototype utilises distributed technology and built upon the research carried out

Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The electroencephalograph (EEG) signal is one of the most widely used signals in the biomedicine field due to its rich information about human tasks. This research study describes a new approach based on i) build reference models from a set of time series, based on the analysis of the events that they contain, is suitable for domains where the relevant information is concentrated in specific regions of the time series, known as events. In order to deal with events, each event is characterized by a set of attributes. ii) Discrete wavelet transform to the EEG data in order to extract temporal information in the form of changes in the frequency domain over time- that is they are able to extract non-stationary signals embedded in the noisy background of the human brain. The performance of the model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals

DISTRIBUTED ONLINE DOCTOR SURGERY

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Development of a decision support framework for electroencephalography signals based on an adaptive fuzzy inference neural network system

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