Churn prediction is a useful tool to predict customer at churn risk. By accurate prediction of churners and non-churners, a company can use the limited marketing resource efficiently to target the churner customers in a retention marketing campaign. Accuracy is not the only important aspect in evaluating a churn prediction models. Churn prediction models should be both accurate and comprehensib...

A method based on adaptive neuro-fuzzy inference system (ANFIS) for computing the effective permittivity and the characteristic impedance of the micro-coplanar strip (MCS) line is presented. The ANFIS is a class of adaptive networks which are functionally equivalent to fuzzy inference systems (FISs). A hybrid learning algorithm, which combines the least square method and the backpropagation alg...

This paper introduces the application of the hybrid approach Adaptive Neuro-Fuzzy Inference System (ANFIS) for fault classification and diagnosis in industrial actuator. The ANFIS can be viewed either as a fuzzy inference system, a neural network or fuzzy neural network (FNN). This paper integrates the learning capabilities of neural network to the robustness of fuzzy systems in the sense that ...

Purpose Respiratory motion prediction is a chaotic time series prediction problem. In this study, respiratory motion predictability from 12 traces from breast cancer patients is examined by using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Interval Type-2 Non Singleton Fuzzy System (IT2NSFLS). Methods Free breathing data curves were obtained from Real Time Position Management system (RPM ...

Adaptive Neuro-fuzzy inference system (ANFIS) is multi-layer system proposed by Jang, enables to increase the performance by integrates the best features of Artificial Neural Networks and Fuzzy inference system into a single framework. It is a popular framework for solving complex problems. In this present work, an ANFIS is proposed for detection the risk factor of hypertension. In this work, f...

This paper presents an optimal load balancing algorithm based on both of the ANFIS (Adaptive Neuro-Fuzzy Inference System) modeling and the FIS (Fuzzy Inference System) for the local status of real servers. It also shows the substantial benefits such as the removal of loadscheduling overhead, QoS (Quality of Service) provisioning and providing highly available servers, provided by the suggested...

BOD is a parameter frequently used to evaluate the water quality on different rivers. The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adapti ve Neuro-Fuzzy Inference System) in water quality BOD prediction for the case study, Mahi river at Khanpur in Thasara Taluka of Kheda District in Gujarat State, India. The proposed technique...

In this work, an artificial neural network (ANN) model along with a combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) i.e. (PSO-ANFIS) are proposed for modeling and prediction of the propylene/propane adsorption under various conditions. Using these computational intelligence (CI) approaches, the input parameters such as adsorbent shape (S<su...

The application of Artificial Intelligent approaches was introduced recently in protection of distribution networks. These approaches started with introducing Fuzzy Inference System (FIS), then using Artificial Neural Network (ANN).In this research, the application of Adaptive Neuro Fuzzy Inference System (ANFIS) for protection of bus bars will be illustrated. The ANFIS can be viewed as a fuzzy...

the impact of air pollution and environmental issues on public health is one of the main topics studied in manycities around the world. ozone is a greenhouse gas that contributes to global climate. this study was conducted topredict and model ozone of yazd in the lower atmosphere by an adaptive neuro-fuzzy inference system (anfis). allthe data were extracted from 721 samples collected daily ove...