The objective of the study was to optimize the formulation parameters of cytarabine liposomes by using artificial neural networks (ANN) and multiple regression analysis using 3(3) factorial design (FD). As model formulations, 27 formulations were prepared. The formulation variables, drug (cytarabine)/lipid (phosphatidyl choline [PC] and cholesterol [Chol]) molar ratio (X1), PC/Chol in percentage ratio of total lipids (X2), and the volume of hydration medium (X3) were selected as the independent variables; and the percentage drug entrapment (PDE) was selected as the dependent variable. A set of causal factors was used as tutorial data for ANN and fed into a computer. The optimization was performed by minimizing the generalized distance between the predicted values of each response and the optimized one that was obtained individually. In case of 3(3) factorial design, a second-order full-model polynomial equation and a reduced model were established by subjecting the transformed values of independent variables to multiple regression analysis, and contour plots were drawn using the equation. The optimization methods developed by both ANN and FD were validated by preparing another 5 liposomal formulations. The predetermined PDE and the experimental data were compared with predicted data by paired t test, no statistically significant difference was observed. ANN showed less error compared with multiple regression analysis. These findings demonstrate that ANN provides more accurate prediction and is quite useful in the optimization of pharmaceutical formulations when compared with the multiple regression analysis method.
Grammatical feature (POS) Labeling is a testing undertaking to distinguish the significance of each word in a sentence. This paper shows the assignment of distinguishing Grammatical form TAG for each transform in a Guajarati sentence utilizing the system of support Vector Machine and Viterbi deciphering method. Guajarati corpus of 1700 words is taken and tried it precisely. Labeling is done utilizing Viterbi and SVM and the outcome is examined in four classifications. In every one of the classifications Viterbi based method gives much better correctness's.
The paper presents an oversampling technique to overcome the loss incurred at the time of binarizing the images. As such there are very few techniques available in the literature to approximate the loss in the images. This technique is based on the Estimate wavelet transform (EWT) which fulfills the loss and makes the image smooth. Coiflet and Daubechies D8 wavelets are employed in EWT and performances are compared with the traditional Gaussian lowpass filter. All the three approaches are tested on seven distorted images, and observed that the Daubechies D8 and Coiflet wavelets using EWT are better able to approximate the image than Gaussian lowpass filter. Out of seven images two are presented in this paper.
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