Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are demonstrated to be competent when applied individually to a variety of problems. Recently, there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have been evolved. In this study, a new approach based on an adaptive neuro-fuzzy infer...

Tumor is an uncontrolled growth of tissue in any part of the body. The tumor is of different types and they have different characteristics and different treatment. Normally the anatomy of the brain can be viewed by the MRI scan or CT scan. MRI scanned image is used for the entire process. The MRI scan is more comfortable than any other scans for diagnosis. It will not affect the human body, bec...

Image segmentation in the medical domain is a challenging field owing to poor resolution and limited contrast. The predominantly used conventional segmentation techniques and the thresholding methods suffer from limitations because of heavy dependence on user interactions. Uncertainties prevalent in an image cannot be captured by these techniques. The performance further deteriorates when the i...

Software quality and reliability have become the main concern during the software development. It is very difficult to develop software without any fault. The fault-proneness of a software module is the probability that the module contains faults and a software fault is a defect that causes software failures in an executable project. Early detection of fault prone software components enables ve...

The development of empirical classification models for fault diagnosis usually requires a process of training based on a set of examples. In practice, data collected during plant operation contain signals measured in faulty conditions, but they are ‘unlabeled’, i.e., the indication of the type of fault is usually not available. Then, the objective of the present work is to develop a methodology...

One of the main problems in the management of large water supply and distribution systems is the forecasting of daily demand in order to schedule pumping effort and minimize costs. This paper examines a methodology for consumer demand modeling and prediction in a real-time environment of an irrigation water distribution system. The approach is based on Adaptive Neuro-Fuzzy Inferences System (AN...

In this paper, an attempt has been made to design an computational intelligence technique based expert system using Adaptive Neuro-Fuzzy Inference System (ANFIS) for predicting surface roughness in end milling of Inconel 718. Two different types of membership functions are adopted for analysis in ANFIS training and compared their differences regarding the accuracy rate of the surface roughness ...

In this paper, we apply K-means and Fuzzy C-Means, two widely used clustering algorithms, to cluster a lymph node tissue section which had been diagnosed with metastatic infiltration (cancer spread from its original location). Each cluster algorithm is run 10 times as different initialisation states may lead to different clustering results. We compare the performance of the two algorithms by su...

Image segmentation and classification are two important steps in multicolor fluorescence in-situ hybridization (MFISH) images analysis. In this paper we first developed an improved adaptive fuzzy c-means (IAFCM) algorithm for the segmentation of the DAPI channel of M-FISH images to extract interested regions for the following classification task. Then we employed a sparse representation-based c...

-Now a day we have various types of intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are proving to be skillful when applied to a different kind of problems. This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. here we applied tool for detecting the two dif...