Abstract-Edge detection is the vital task in digital image processing. It makes the image segmentation and pattern recognition more comfort. It also helps for object detection. There are many edge detectors available for pre-processing in computer vision. But, Canny, Sobel, Laplacian of Gaussian (LoG), Robert's and Prewitt are most applied algorithms. This paper compares each of these operators by the manner of checking Peak signal to Noise Ratio (PSNR) and Mean Squared Error (MSE) of resultant image. It evaluates the performance of each algorithm with Matlab and Java. The set of four universally standardized test images are used for the experimentation. The PSNR and MSE results are numeric values, based on that, performance of algorithms identified. The time required for each algorithm to detect edges is also documented. After the Experimentation, Canny operator found as the best among others in edge detection accuracy.

No abstract

Support Vector Machine (SVM) is a popularmachine learning method for classification, regression, andother learning tasks. Support Vector Regression (SVR), acategory for support vector machine attempts to minimize thegeneralization error bound so as to achieve generalizedperformance. Regression is that of finding a function whichapproximates mapping from an input domain to the realnumbers on the basis of a training sample. Support vectorregression is the natural extension of large margin kernelmethods used for classification to regression analysis. In thispaper Support Vector Regression is used to forecast thedemand and supply of pulpwood. The usage of paper increasesday to day. Wood Pulp is the most common raw material inpaper making. On account of steady increase in paper demand,the forecast on demand and supply of pulp wood is consideredto improve the socio economic development of India.Forecasting is done in Libsvm a library for support vectormachines by integrating it with MATLAB

No abstract

No abstract