practice phase. Take Artificial Neutral Network for example. An Artificial Neural Network (ANN) is a mathematical model or computational model. Though ANN is already a classical technique frequently applied in fields like computer vision‚ natural language processing‚ optimized computing‚ pattern recognition‚ automatic control‚ and development of neural computer all of which are branches of computer science‚ it is inspired by the structure and functional aspects of biological neural networks. As a matter
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It is a well-known fact that Sri Lankans life style has been drastically changed recently. Health department’s researches corroborate the above. As result of the change of life style people have unknowingly neglected the value of balance‚ nutritious diet and they have forgotten the importance of the schedule of daily exercises that enables to keep a healthy body. I do say‚ people both rich and poor are unaware of the values of three main meals. Particularly‚ rich people tend to consume instant foods
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Real Time Road Sign Recognition System Using Artificial Neural Networks For Bengali Textual Information Box An Automated Road Sign Recognition system using Artificial Neural Network for the Textual Information box inscribing in Bengali is presented on the paper. Signs are visual languages that represent some special circumstantial information of environment. Road signs‚ being among the most important around us primarily for safety reasons‚ are designed‚ and manufactured and installed according
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Therefore‚ LSPTAN builds a simpler network than the SP-TAN. We select only the best Super Parent to a test document. But there is no limitation on the choice of the Favorite Children. Thus‚ all the children attributes that increment the probability that the document belongs to a class‚ are
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УДК 681.3.01+681.327.12 http://neuroface.narod.ru ACCESS CONTROL BY FACE RECOGNITION USING NEURAL NETWORKS* Dmitry Bryliuk and Valery Starovoitov Institute of Engineering Cybernetics‚ Laboratory of Image Processing and Recognition Surganov str.‚ 6‚ 220012 Minsk‚ BELARUS E-mail: bdv78@mail.ru‚ valerys@newman.bas-net.by A Multilayer Perceptron Neural Network (NN) is considered for access control based on face image recognition. We studied robustness of NN classifiers with respect to the False
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COGNITIVE COMPUTING- IBM WATSON The term cognitive computing has been used to refer to new hardware and/or software that mimic the functioning of the human brain. Like a human‚ a cognitive computing application learns by experience and/or instruction. The CC application learns and remembers how to adapt its content displays‚ by situation‚ to influence behaviour. This means a CC application must have intent‚ memory‚ foreknowledge and cognitive reasoning for a domain of variable situations. Cognitive-based
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4. ANFIS: On the basis of Takagi-Sugeno Fuzzy interference system the ANFIS or the Adaptive Network Based Fuzzy Inference is one kind of AI or Artificial Intelligence. In the 1990s this technique was developed as it combines the two forms the Fuzzy Logic Principles and as well as the Neural Networks. It can gather both the framework. The interference system correlates to the set of the IF-THEN functions which are non linear to be approximated using this rules it has a capability to learn. Thus it
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SHORT-TERM LOAD FORECASTING USING ARTIFICIAL NEURAL NETWORK TECHNIQUES A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF BACHELOR OF TECHNOLOGY In Electrical Engineering By MANOJ KUMAR ROLL NO. 10502053 Department of Electrical Engineering National Institute of Technology Rourkela‚2009 i SHORT-TERM LOAD FORECASTING USING ANN TECHNIQUE LOAD FORECASTING USING ARTIFICIAL NEURAL NETWORK TECHNIQUES A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE
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step of fuzzy inference process of TSK method‚ fuzzifying inputs and applying the fuzzy operator‚ are exactly the same. The most fundamental difference between these two models are the way the crisp output is generated from the fuzzy inputs [1]. TSK network uses weighted average to compute the crisp output instead of the defuzzification step in Mamdani model which is time consuming. Then together with the fact that in most cases TSK model has less fuzzy rules than Mamdani method make TSK model a more
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Azzedine Boukerche2 Fernando A. S. Cruz1 Bernardo G. Riso1 Carlos B. Westphall1 1 Network and Management Laboratory Federal University of Santa Catarina (UFSC) {mirela‚ cruz‚ riso‚ westphal}@lrg.ufsc.br 2 Department of Computer Sciences University of North Texas boukerche@silo.csci.unt.edu Abstract: This work presents the development of a distributed security management system for telecommunication networks. The system consists in reducing the use of cloned mobile telephones using three main
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