This paper presents a diagnosis system, based on an adaptive neuro-fuzzy inference system (ANFIS) algorithm, for applications in biomedical fields. This paper deals specifically with skin cancer diagnosis. Our system can be divided into two main parts: feature selection, using the Greedy feature flip algorithm (G-flip), and Classification method using ANFIS algorithm. The ANFIS algorithm could ...

This paper presents a fault identification, classification and fault location estimation method based on Discrete Wavelet Transform and Adaptive Network Fuzzy Inference System (ANFIS) for medium voltage cable in the distribution system. Different faults and locations are simulated by ATP/EMTP, and then certain selected features of the wavelet transformed signals are used as an input for a train...

In this paper, Wavelet based ANFIS for finding inter turn fault of generator is proposed. The detector uniquely responds to the winding inter turn fault with remarkably high sensitivity. Discrimination of different percentage of winding affected by inter turn fault is provided via ANFIS having an Eight dimensional input vector. This input vector is obtained from features extracted from DWT of i...

In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of ophthalmic artery stenosis. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The ophthalmic arterial Doppler signals...

Stock market prediction is important and of great interest because successful prediction of stock prices may promise attractive benefits. These tasks are highly complicated and very difficult. In this paper, we investigate the predictability of stock market return with Adaptive Network-Based Fuzzy Inference System (ANFIS). The objective of this study is to determine whether an ANFIS algorithm i...

This paper presents an experimental evaluation of a suite of dynamic partitioning/load-balancing techniques for adaptive grid hierarchies that underlie parallel structured adaptive mesh re nement applications. Partitioners evaluated include those included in popular software libraries such as GrACE, Vampire, and ParMetis. The overall goal of the research presented in the paper is an application...

In this paper we present the parallel implementation of the Princeton Ocean Model (POM) using message passing. Domain decomposition techniques are used for the horizontal discretization whereas in the vertical direction each column per grid point is computed. The required interprocessor communication was implemented on the PVM message passing library. Three different data partitioning schemes a...

Wind energy has increasingly played a vital role in mitigating conventional resource shortages. Nevertheless, the stochastic nature of wind poses a great challenge when attempting to find an accurate forecasting model for wind power. Therefore, precise wind power forecasts are of primary importance to solve operational, planning and economic problems in the growing wind power scenario. Previous...

The Computational Grid has emerged as an attractive platform for developing large-scale distributed applications that run on heterogeneous computing resources. This scalability, however, comes at the cost of increased complexity: each application has to handle the details of resource provisioning and data and task scheduling. To address this problem, we present GRID*p an interactive parallel sy...

Accurate solar radiation (SR) prediction is one of the essential prerequisites harvesting energy. The current study proposed a novel intelligence model through hybridization Adaptive Neuro-Fuzzy Inference System (ANFIS) with two metaheuristic optimization algorithms, Salp Swarm Algorithm (SSA) and Grasshopper Optimization (GOA) (ANFIS-muSG) for global SR at different locations North Dakota, USA...