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Articles by K. Dinakaran
Total Records ( 2 ) for K. Dinakaran
  D. Rajalakshmi and K. Dinakaran
  In real time application such as weather forecasting, coal mine surveillance and privacy preserving data streams arrive at a rate higher than in traditional sensing applications. The processing of these raw data must be as fast as stream speed. These applications convert the raw data into a specified pattern and these patterns vary from application to application. It is then subjected to sophisticated query processing to extract high level information. Uncertainty in stream time series may occur for two reasons such as the inherent imprecision of sensor readings or privacy preserving conversion. Representation of uncertain data over stream time-series, uncertain data management and designing of a data mining algorithm on considering the uncertainty affects the data mining process. The processing of data in the real time application is difficult as it is naturally incomplete and noisy and the observed data pattern is different from the actual pattern required for further processing. A major challenge in processing the stream time-series data with uncertainty is to capture uncertainty as data propagates through query operators until the final result and to process the data at stream speed. The modeling of uncertain time-series without affecting the precision of the system still remains a difficult task. More importantly, it should avoid increased false positives. In this study, a survey of time series data management for pattern matching is provided. The similarity search over uncertain data will be explored. The issues in existing research, limitations and methodology for pattern matching on uncertain time series stream data are examined in this study.
  K. Dinakaran and R. Preethi
  A medical record in general is a systematic documentation of a single patient's long-term individual medical history and treatment. In medical field, patient records are used for analysing their health problem. Clinical dataset is the essential medical record which deals with patient’s health details. In the traditional method, medication can be provided to only one patient at a time and it is difficult to identify group of the people having similar symptoms. Multiple health assessment is time consuming and impractical. The present study proposes a new methodology to find potential information related to blood oriented diseases. Generally, real world Complete Blood Count data are susceptible to noise and not suitable for computation. So, there is a need for data pre-processing. Among the refining techniques, data transformation method such as normalisation and data recoding are applied on relevant attributes of Complete Blood Count. Grouping of people having similar health problems can be done by unsupervised learning. Expectation Maximization Clustering algorithm and k-Means clustering algorithm together clusters effectively the patients based on the attributes. It is shown that refined data produces optimum result and may be useful for medical community to diagnose a group of patients.
 
 
 
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