the main goal of research is designing an adaptive nuero-fuzzy inference system for evaluating the implementation of business intelligence systems in software industry. iranian software development organizations have been facing a lot of problems in case of implementing business intelligence systems. this system would be helpful in recognizing the conditions and prerequisites of success or fail...

System modelling based on conventional mathematical tools like differential equations is not well suited for dealing with ill-defined and uncertain systems. By contrast, a fuzzy inference system, employing fuzzy if–then rules can model the qualitative aspects of human knowledge and reasoning processes without employing precise quantitative analyses. This fuzzy modeling or fuzzy identification e...

This paper proposes a new hybrid time series forecasting system which is the fusion of the fuzzy system and the artificial neural network. The proposed fuzzy-neural system consists of 5 layers: the input layer, the fuzzification layer, the inference layer, the hidden layer, and the output layer. The artificial neural network is used as the fuzzy inference engine, while the genetic algorithm is ...

Disease diagnosis is a challenging task due to large number of associated factors. Uncertainty in the process arises from inaccuracy patient attributes, missing data, and limitation medical expert's ability define cause effect relationships when there are multiple interrelated variables. This paper aims demonstrate an integrated view deploying smart disease using Internet Things (IoT) empowered...

This paper presents a new general purpose neuro-fuzzy controller to realize adaptive-network-based fuzzy inference system (ANFIS) architecture. ANFIS which tunes the fuzzy inference system with a back propagation algorithm based on collection of input-output data makes fuzzy system to learn. To implementing this idea we propose several improved CMOS analog circuits, including Gaussian-like memb...