Biblio

Data is a collection of information from the activities of the real world. The file in which such data is stored after transforming into a form that machines can process is generally known as data set. In the real world, many data sets are not complete, and they contain various types of noise. Missing values is of one such kind. Thus, imputing data of these missing values is one of the significant task of data pre-processing. This paper deals with two real time health care data sets namely life expectancy (LE) dataset and chronic kidney disease (CKD) dataset, which are very different in their nature. This paper provides insights on various data imputation techniques to fill missing values by analyzing them. When coming to Data imputation, it is very common to impute the missing values with measure of central tendencies like mean, median, mode Which can represent the central value of distribution but choosing the apt choice is real challenge. In accordance with best of our knowledge this is the first and foremost paper which provides the complete analysis of impact of basic data imputation techniques on various data distributions which can be classified based on the size of data set, number of missing values, type of data (categorical/numerical), etc. This paper compared and analyzed the original data distribution with the data distribution after each imputation in terms of their skewness, outliers and by various descriptive statistic parameters.

Harmonic distortions come into existence in the power system not only due to nonlinear loads of consumers but also due to custom power devices used by power utilities. These distortions are harmful to the power networks as these produce over heating of appliances, reduction in their life expectancy, increment in electricity bill, false tripping, etc. This paper presents an effective, simple and direct approach to identify the problematic cause either consumer load or utility source or both responsible for harmonics injection in the power system. This technique does not require mathematical model, historical data and expert knowledge. The online methodology is developed in the laboratory and tested for different polluted loads and source conditions. Experimental results are found satisfactory. This proposed technique has substantial potential to determine the problematic cause without any power interruption by plug and play operation just like CCTV.