In this paper the node-level decision unit of a self-learning anomaly detection mechanism for office monitoring with wireless sensor nodes is presented. The node-level decision unit is based on Adaptive Resonance Theory (ART), which is a simple kind of neural networks. The Fuzzy ART neural network used in this work is an ART neural network that accepts analog inputs. A Fuzzy ART neural network ...

|Two training algorithms for self evolving neural networks are discussed for rule based data analysis. E cient classi cation is achieved with less number of automatically added clusters and application data is analysed by interpreting the trained neural network as a fuzzy rule based system. Learning Vector Quantisation algorithm has been modied acquiring the self evolvement character in the pro...

Signature is used for recognition of an individual. considered as a mark that individual write on paper his/her identity or proof. It unique feature identifying highly in social and business functions which gives rise to verification signature. There are chances signature getting forged. Hence, the need identify genuine forged utmost important. In this paper, identification done using two appro...

Soil cation exchange capacity (CEC) is a parameter that represents soil fertility. Being difficult to measure, pedotransfer functions (PTFs) can be routinely applied for prediction of CEC by soil physicochemical properties that can be easily measured. This study developed the support vector regression (SVR) combined with genetic algorithm (GA) together with the adaptive network-based fuzzy infe...

Bubble point pressure is an important parameter in equilibrium calculations of reservoir fluids and having other applications in reservoir engineering. In this work, an artificial neural network (ANN) and a least square support vector machine (LS-SVM) have been used to predict the bubble point pressure of reservoir fluids. Also, the accuracy of the models have been compared to two-equation stat...

Image classification is a process that may be affected by many factors. This paper is in regard of the classification techniques used for image processing and analysis using the concept of Fuzzy and Possibilities techniques, applied in each pixel of false-color satellite image By this paper examine classification approaches and the technique used for improving classification accuracy. By the in...

We introduce a fuzzy rough granular neural network (FRGNN) model based on the multilayer perceptron using a back-propagation algorithm for the fuzzy classification of patterns. We provide the development strategy of the network mainly based upon the input vector, initial connection weights determined by fuzzy rough set theoretic concepts, and the target vector. While the input vector is describ...

The classification of Internet traffic has come to the forefront in recent times as organization of network traffic is necessitated by the increasing use of the internet and limited bandwidth. Also,