COMPARATIVE CLINICAL EVALUATION OF THE EFFECT OF MATRAVASTI AND KATIVASTI WITH DHANVANTARA TAILA AND TILA TAILA IN THE MANAGEMENT OF GRIDHRASI VATA VIS-A-VIS SCIATICA

Many Vata vyadhi are described in Charaka Samhita which are classified into Nanatmaja and Samanyaja group. Gridhrasi is one among 80 Vataja nanatmaja vyadhi. This can be correlated with Sciatica in modern medicine. Panchakarma presents a unique approach of Ayurveda. Among the Panchakarma, Vasti karma is such a Chikitsa that is indicated in all the Vatavyadhi. Gridhrasi is a Vata vyadhi, in which local Samprapti is having quiet major importance. In the procedure of Kativasti, Snehana and Svedana occur simultaneously and locally. So Matravasti and Kativasti have an important role as both come under Snehana treatment and have been selected for the study. Dhanvantara taila is indicated in Vata vyadhi and Tila taila is Marutaghnam, so both the Taila are selected for the study. In the present clinical study, 60 patients with Gridhrasi under inclusion criteria are selected and divided into four groups. The treatment is for 14 days. The efficacy of treatment is assessed immediately after treatment and after 15 days of completion of treatment by objective parameters and by adopting scoring methods for the subjective parameters and the results are analyzed statistically by ANOVA test. The results of the study indicate that the ‘p’ value is highly significant to extremely significant in all the four groups in selected parameters

Modeling of Machining Parameters in CNC End Milling Using Principal Component Analysis Based Neural Networks

The present paper uses the principal component analysis (PCA) based neural networks for predicting the surface roughness in CNC end milling of P20 mould steel. For training and testing of the neural network model, a number of experiments have been carried out using Taguchi's orthogonal array in the design of experiments (DOE). The cutting parameters used are nose radius, cutting speed, cutting feed, axial depth of cut and radial depth of cut. The accurate mathematical model has been developed using PCAs networks. The adequacy of the developed model is verified using coefficient of determination (R). It was found that the R2 value is 1. To judge the ability and efficiency of the neural network model, percentage deviation and average percentage deviation has been used. The research showed acceptable prediction results for the neural network model

Application of Soft Computing for the Prediction of Warpage of Plastic Injection

This paper deals with the development of accurate warpage prediction model for plastic injection molded parts using softcomputing tools namely, artificial neural networks and support vector machines. For training, validating and testing of thewarpage model, a number of MoldFlow (FE) analyses have been carried out using Taguchi’s orthogonal array in the designof experimental technique by considering the process parameters such as mold temperature, melt temperature, packing pressure,packing time and cooling time. The warpage values were found by analyses which were done by MoldFlow PlasticInsight (MPI) 5.0 software. The artificial neural network model and support vector machine regression model have beendeveloped using conjugate gradient learning algorithm and ANOVA kernel function respectively. The adequacy of the developedmodels is verified by using coefficient of determination. To judge the ability and efficiency of the models to predictthe warpage values absolute relative error has been used. The finite element results show, artificial neural network modelpredicts with high accuracy compared with support vector machine model

Nonlinear Thermal Analysis of Functionally Graded Plates Using Higher Order Theory

In this paper, the nonlinear thermal analysis of functionally graded material (FGM) plate with material variation parameter (n), boundary conditions, aspect ratios and side to thickness ratios is investigated using higher order displacement model. The derivation of equations of motion for higher order displacement model is obtained using principle of virtual work. The nonlinear simultaneous equations are obtained by Navier's method considering certain parameters, loads and boundary conditions. The nonlinear algebraic equations are solved using Newton Raphson iterative method. The effect of shear deformation and nonlinearity response of functionally graded material is investigated. Keywords: Nonlinear thermal analysis, FGM plates, higher order theory, Navier's method, Newton Raphson method

A Novel Shoeprint Enhancement method for Forensic Evidence Using Sparse Representation method.

Shoeprints are often recovered at crime scenes and are the most abundant form of evidence at a crime scene, and in some cases, it is proved to be as accurate as fingerprints. The basis for shoeprint impression evidence is determining the source of a shoeprint impression recovered from a crime scene. This shoeprint evidence collected are often noisy and unclear. To obtain a clear image, the shoeprint evidence should be enhanced by de-noising and improving the quality of the picture. In the thesis, we introduced a novel shoeprint enhancement algorithm based on sparse representation for obtaining the complete dictionary from a set of shoeprint patches which allows us to represent them as a sparse linear combination of dictionary atoms. In the proposed algorithm, we first pre-process the image by SMQT method, and then Daubechies first level DWT is applied. The SVD of the image is computed, and Inverse Discrete Wavelet Transform(IDWT) is applied. To the singular value decomposed image, l1-norm minimization sparse representation employed by the K-SVD algorithm is computed where the image is divided into predefined shoeprint image patches of size 8 by 8. Shoeprint images of three different databases with different image quality are tested. The performance of the algorithm is assessed by comparing the original shoeprint image and the image obtained after proposed algorithm based on objective and subjective parameters like PSNR, MSE, and MOS. The results show the proposed method gives better performance in terms of contrast (Variance) and brightness (Mean). Finally, as a conclusion, we state that the proposed algorithm enhances the image better than the existing method DWT-SVD.  

A Novel Shoeprint Enhancement method for Forensic Evidence Using Sparse Representation method.

Shoeprints are often recovered at crime scenes and are the most abundant form of evidence at a crime scene, and in some cases, it is proved to be as accurate as fingerprints. The basis for shoeprint impression evidence is determining the source of a shoeprint impression recovered from a crime scene. This shoeprint evidence collected are often noisy and unclear. To obtain a clear image, the shoeprint evidence should be enhanced by de-noising and improving the quality of the picture. In the thesis, we introduced a novel shoeprint enhancement algorithm based on sparse representation for obtaining the complete dictionary from a set of shoeprint patches which allows us to represent them as a sparse linear combination of dictionary atoms. In the proposed algorithm, we first pre-process the image by SMQT method, and then Daubechies first level DWT is applied. The SVD of the image is computed, and Inverse Discrete Wavelet Transform(IDWT) is applied. To the singular value decomposed image, l1-norm minimization sparse representation employed by the K-SVD algorithm is computed where the image is divided into predefined shoeprint image patches of size 8 by 8. Shoeprint images of three different databases with different image quality are tested. The performance of the algorithm is assessed by comparing the original shoeprint image and the image obtained after proposed algorithm based on objective and subjective parameters like PSNR, MSE, and MOS. The results show the proposed method gives better performance in terms of contrast (Variance) and brightness (Mean). Finally, as a conclusion, we state that the proposed algorithm enhances the image better than the existing method DWT-SVD.  

Knowledge Discovery from Static Datasets to Evolving Data Streams and Challenges

Mining data streams has recently become an important active research work and more widespread in several fields of computer science and engineering. It has proven successfully in many domains such as wireless sensor networks, ATM transactions, search engines, web analysis and weather monitoring. Data steams can be considered a subfield of machine learning, data mining and knowledge discovery. Data Mining is a step in the process of knowledge discovery from large amount of data. Traditional data mining techniques can not be easily applied to the data stream mining due to unique characteristics of data streams. In this research work, we will survey the main techniques and applications of data mining and data stream mining. We then study, the computational and miming challenges in particular, on-line mining of continuous, high-speed massive data streams