Artificial Neural Networks have been widely used for the purpose of medical diagnosis in the last decades. The diagnosis of diseases such as thyroid using artificial neural networks is an important research area because of the need of more and more accuracy in the crucial process of disease diagnosis. This paper presents a comparison of two artificial neural network algorithms viz. Multilayer Back Propagation (BPN) -a supervised approach and Self Organizing Maps (SOM) -an unsupervised approach for the diagnosis of thyroid disease using real patient data. It has been found in this study that the results of unsupervised SOM network performed equally well with 100% accuracy as the supervised BPN network in less training time but with a comparatively large percentage of training data.

Abstract-In the past decade, Improve the quality in face recognition system is a challenge. It is a challenging problem and widely studied in the different type of images to provide the best quality of faces in real life. These problems come due to illumination and pose effect due to light in gradient features. The improvement and optimization of human face recognition and detection is an important problem in the real life that can be handles to optimize the error rate, accuracy, peak signal to noise ratio, mean square error, and structural similarity Index. Now-a-days, there several methods are proposed to recognition face in different problem to optimize above parameters. There occur many invariant changes in human faces due to the illumination and pose variations. In this paper we proposed a novel method in face recognition to improve the quality parameters using speed up robust feature and linear discriminant analysis for optimize result. SURF is used for feature matching. In this paper, we use linear discriminant analysis for the edge dimensions reduction to live faces from our data-sets. The proposed method shows the better result as compare to the previous result on the basis of comparative analysis because our method show the better quality and better results in live images of face.

In the recent research work, the handwritten signature is a suitable field to detection of valid signature from different environment such online signature and offline signature. In early research work, a lot of unauthorized person put the signature and theft the data in illegal manner from organization or industries. So we have to need identify, the right person on the basis of various parameters that can be detected. In this paper, we have proposed two methods namely LDA and Neural Network for the offline signature from the scan signature image. For efficient research, we have focused the comparative analysis in terms of FRR, SSIM, MSE, and PSNR. These parameters are compared with the early work and the recent work. Our proposed work is more effective and provides the suitable result through our method which leads to existing work. Our method will help to find legal signature of authorized use for security and avoid illegal work.