Design Of Hook On Pillar For Solving Partial Differential Equations

The use of waves in engineering can be seen from two perspectives, first examining the response of the device to remove common parts, rate of damage, sound, etc., and secondly, comparison of comparisons that control the machine tool. Wavelet theory provides various basic functions and multi-precision methods for finishing elemental methods. The wave-dependent element can be built using the Daubechies scale as a function. In this article, a compressively supported wave-supporting Daubechies solution to the borderline problem of uncertainty details of prismatic organs is presented. This problem can be misunderstood by the Wavelet is used for data analysis, signalling, image modelling, as well as for instability timelines. Wavelengths are relevant to numerical needs and are used in other functions. T-Gale approach. The evaluation of network connections plays an important role in the use of wavelet gale kin methods for solving computational differences. The problem of installing control rods is explained using the Wavelet-Gale kin method in this article. Comparisons are made with detailed responses and elemental results. Now research shows that wavelet technology provides another good way to the finite element method

IMAGE RETRIEVAL FROM DATABASE USING DIFFERENT IMAGE FEAT

Image retrieval from the database is emerged due to the increased growth in number of images and also the application in several fields. Color, Edge, Texture, Contour, Autocorrelogram, Color Moments, Gabor wavelet are the some of features that represent the images and also these features are utilized for indexing of image. These features are seen as global and local. Gabor wavelet is used to extract texture feature. These features are referred as local features of an image. Color component is also a one of the feature called as global feature.HSV color model is used in this paper. Matching algorithm is used to match similarity between features stored in the database and query image features. The paper deals with image retrieval from the database using combined feature. Columbia Object Image Library (COIL) is a database used in this project.COIL-100 database is used for the experimental purpose

Effect of Vitamin E and omega 3 fatty acids in type 2 diabetes mellitus patients

Diabetes mellitus (DM) and its complications have been implicated in hyperglycemia-induced oxidative stress. Antioxidants can improve glycemic control, lipid profile, and cognitive functions. We assessed the effect of Vitamin E and omega 3 fatty acids (OFA) on the above parameters. One hundred patients with type 2 DM receiving metformin 500 mg and glimepiride 1 mg were randomized to receive add-on therapy of Vitamin E 400 mg or OFA once daily for 12 weeks and the third group served as control. Fasting blood sugar (FBS), postprandial blood sugar (PPBS), glycated hemoglobin (HbA1c), body mass index (BMI), waist-hip ratio (WHR), lipid profile, and mini-mental state examination were done at baseline and 12 weeks. Eighty-seven patients completed the study. A significant reduction in FBS, PPBS, and HbA1c was observed in all the three groups at 12 weeks. There was significant reduction in total cholesterol and triglycerides (TG) in patients receiving either of the antioxidants and also significant reduction in low-density lipoprotein in patients receiving OFA at 12 weeks compared to baseline. BMI and WHR were significantly increased in control group. Intergroup analysis showed that in patients receiving Vitamin E and OFA, the reduction of FBS, PPBS, and HbA1c were similar. The patients receiving OFA had significant reduction in TG compared to control. There was no significant effect on cognitive function. Vitamin E and OFA had beneficial effects on lipid profile and anthropometric measurements; however, the glycemic control was similar to the patients in control group

Detection of Face Recognition of Interviewee Using Transform Technique and Machinle Learning Algorithm

A crucial task in any firm is the hiring of new personnel. Virtual interviews have replaced face-to-face interviews as the norm since the Pandemic. Knowing the sincerity of the interviewee while applying to the company becomes a significant task in such a situation. The practice of manually comparing a candidate's face from many interview rounds to the actual candidate joining the organization is being used by interviewers. I want to automate this human process, using machine learning techniques to aid the interviewee's sincerity be established. Machine learning techniques will be used in this procedure to find and identify faces in pictures taken during the first round of interviews. Then compare it later to the real face that was photographed at the time of joining. If all of the visuals line up, it establishes the interviewee's sincerity. And if they don't match, management can take the necessary steps offline. This project will be conceived up and explored from the standpoint of how and whether Python may be used to implement