It is realized that innovative progressions are expanding at a quicker pace. In any case, the usage of these innovations is less in different parts. It is realized that the general population of nowadays need help for doing a few works when they were matured. .It is troublesome for visually impaired individuals to recognize these articles whenever lostinside a home. But since of these actualities, we may encounter different pressure and issues identified with the loss of those items for individuals. So, we propose a framework where we candistinguishthelostarticleswiththeassistanceofourproposedfram eworkutilizingImagehandlingprocedures.Thatadditionallyreadyto supporttheindividualdependentontheface recognized and confine or personal the passage of obscure people. The current frameworkdistinguishesthelostarticleswiththeassistanceofGPS.Ye t,theuseoffaceacknowledgement for individual recognizable proof by robots is not being used. It doesn't give proficient yield. This causes different slack in the circuit. So we propose a framework to upgrade the item following.
In this work, a novel hybrid neuro-fuzzy classifier (HNFC) technique is proposed for producing more accuracy in input data classification. The inputs are fuzzified using a generalized membership function. The fuzzification matrix helps to create connectivity between input pattern and degree of membership to various classes in the dataset. According to that, the classification process is performed for the input data. This novel method is applied for ten number of benchmark datasets. During preprocessing, the missing data is replaced with the mean value. Then, the statistical correlation is applied for selecting the important features from the dataset. After applying a data transformation technique, the values normalized. Initially, fuzzy logic has been applied for the input dataset; then, the neural network is applied to measure the performance. The result of the proposed method is evaluated with supervised classification techniques such as radial basis function neural network (RBFNN) and adaptive neuro-fuzzy inference system (ANFIS). Classifier performance is evaluated by measures like accuracy and error rate. From the investigation, the proposed approach provided 86.2% of classification accuracy for the breast cancer dataset compared to other two approaches.
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