DESIGN AND DEVELOPMENT OF FLOATING PULSATILE DRUG DELIVERY OF LOSARTAN POTASSIUM

Objective: The objective of the present investigation was to the development of floating pulsatile drug delivery system of Losartan potassium (LP) tablets for obtaining no drug release during floating followed by pulsed, rapid drug release to achieve chronotherapeutic release. In hypertension, the risk of getting heart attacks early in the morning is high and therefore, there was need to develop drug delivery, which will release drugs at morning hours and provide efficacious therapy. LP is a short biological half-life (1.5-2.5h) and readily absorbed from the stomach and upper gastrointestinal tract. Methods: Tablet formulation was prepared by press coating of rapid release core tablets and core tablets were further top coated with a buoyant layer of HPMC K4M and sodium bicarbonate. Various grades of HPMC polymer (E5/E15/E50) were used for the pulsatile coating layer. The developed formulations were characterized for physical characteristics, floating lag time, floating time, release lag time, drug content, swelling index, in vitro dissolution studies, DSC and XRD. Results: The FTIR and DSC studies predicted that there was no chemical interaction between drug and excipients. The core tablet coated with HPMC E50 showed a high swelling index and release the drug 97.60±1.2% at 6h. Buoyant layer with 80 mg HPMC K4M and 25 mg sodium bicarbonate gave satisfactory floating lag time. Conclusion: The system showed an excellent lag phase followed by burst release in the distal small intestine, which gives site and time-specific delivery of LP acting as per chronotherapy for treatment of hypertension

Outlier Based Fraud Detection System

Data mining has the vital task of Outlier detection which aims to detect an outlier from given datasets. The analysis or detection of outlier data is referred to as Outlier Mining. In Data mining, outlier detection is the identification of unusual or distant data records that might be require further investigation or analysis. This paper provides the data driven methods for various fraud detection systems based on literature review, fraudulent activities or cases and comparative research. Outlier detection is the technique which discovers such type of data from the given data set. Several techniques of outlier detection have been introduced which requires input parameter from the user. The goal of this proposed work is to partition the input data set into the number of clusters using K-NN algorithm. Then the clusters are given as an input to the outlier detection methods namely cluster based outlier algorithm and Local Outlier Factor Algorithm. The Performance evaluation of this algorithm confirms that our approach of finding local outliers can be practically implemented