Rice is primary food harvests that each and every one person eats in throughout the globe, particularly in Asian nation. It is mostly classified in relation to its texture, color, grain shape etc. In this work, machine vision system is used for rice classification in order to distinguish rice varieties by using some special features like color, harlick and shape. Initially, real rice images are taken from camera for variety of rice such as Basmati rice, IR 18, Ponni, Ponni Leader and Ration rice. These images are taken as input image. Then special preprocessing schemes are introduced like Image thresholding, image enhancement, sharpening and filtering are used to analyze the rice variety. After that feature extraction processes are carried out for both training and testing images. Finally, the multiclass support vector machine (M-SVM) is incorporated to identify the rice variety based on matching between the feature values of training and testing images. These rice classification results such as accuracy and complexity are compared with all other existing classification processes.

The process of partitioning into different objects of an image is segmentation. In different major fields like face tracking, Satellite, Object Identification, Remote Sensing and majorly in medical field segmentation process is very important to find the different objects in the image. To investigate the functions and processes of human boy in radiology magnetic resonance imaging (MRI) will be used. MRI technique is using in many hospitals for the diagnosis purpose widely in finding the stage of a particular disease. In this paper, we proposed a new method for detecting the tumor with enhanced performance over traditional techniques such as K-Means Clustering, fuzzy c means (FCM). Different research methods have been proposed by researchers to detect the tumor in brain. To classify normal and abnormal form of brain, a system for screening is discussed in this paper which is developed with a framework of artificial intelligence with deep learning probabilistic neural networks by focusing on hybrid clustering for segmentation on brain image and crystal contrast enhancement. Feature’s extraction and classification are included in the developing process. Performance in Simulation of proposed design has shown the superior results than the traditional methods.

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To guarantee individual ID and profoundly secure recognizable proof issues, biometric innovations will give more prominent security while improving precision. This new innovation has been done lately because of exchange misrepresentation, security breaks, individual ID, and so on. The excellence of biometric innovation is that it gives an exceptional code to every individual and can’t be duplicated or manufactured by others. So as to conquer the withdrawal of finger impression frameworks, this paper proposed a palm-based individual distinguishing proof framework, a promising and new research region in biometric recognizable proof frameworks in light of their uniqueness, adaptability and a quicker and wide scope of high speeds. It gives higher security on biometric unique mark frameworks with rich highlights, for example, wrinkles, constant brushes, mainlines, details focuses and single focuses. The fundamental motivation behind the proposed palm unique finger impression framework is to actualize a framework with higher exactness and speed up palm unique finger impression acknowledgment for some clients. Here, in this we presented an exceptionally ensured palm print recognizable proof framework with intrigue extraction territory (ROI) with a morphological procedure utilizing a two-way un-crushed or course vector (UDBW) change to separate low-level palm fingerprints enrolled capacities for its vector work (FV) and afterward after correlation is by estimating the separation between the palm transporters and the capacity of the palm and the capacity of the enlisted transport line and palm control. The after effects of the recreation show that the proposed biometric recognizable proof framework gives more noteworthy precision and solid distinguishing proof speed.