An adaptive neuro-fuzzy inference system (ANFIS) was developed using the subtractive clustering technique to study the air demand in low-level outlet works. The ANFIS model was employed to calculate vent air discharge in different gate openings for an embankment dam. A hybrid learning algorithm obtained from combining back-propagation and least square estimate was adopted to identify linear and...

Shear wave velocity (Vs) data are key information for petrophysical, geophysical and geomechanical studies. Although compressional wave velocity (Vp) measurements exist in almost all wells, shear wave velocity is not recorded for most of elderly wells due to lack of technologic tools. Furthermore, measurement of shear wave velocity is to some extent costly. This study proposes a novel methodolo...

A great challenge of research and development activities have recently highlighted in segmenting of the skin cancer images. This paper presents a novel algorithm to improve the segmentation results of level set algorithm with skin cancer images. The major contribution of presented algorithm is to simplify skin cancer images for the computer aided object analysis without loss of significant info...

Segmentation is a fundamental step in image description or classification. In recent years, several computational models have been used to implement segmentation methods but without establishing a single analytic solution. In this paper, the problem of textured images segmentation upon an unsupervised scheme is addressed. Until recently, there has been few interest in segmenting images involvin...

Swelling in compacted soils may lead to some damages structures and buildings. For the sake of reducing such damages, soil swelling should be determined, so as make exhibit adequate resistance against a phenomenon. most cases, fully non-linear relations have been observed between parameters contributing soil. As such, determined via either experimentations or prediction models. However, being e...

In this paper an autonomous feature clustering framework has been proposed for performance and reliability evaluation of an environmental sensor network. Environmental time series were statistically preprocessed to extract multiple semantic features. A novel hybrid clustering framework was designed based on Principal Component Analysis (PCA), Guided Self-Organizing Map (G-SOM), and Fuzzy-CMeans...

Fuzzy clustering algorithms like the popular fuzzy cmeans algorithm (FCM) are frequently used to automatically divide up the data space into fuzzy granules (fuzzy vector quantization). In the context of fuzzy systems, in order to be intuitive and meaningful to the user, the fuzzy membership functions of the used linguistic terms have to fulfill some requirements like boundedness of support or u...

Clustering is an extensively studied data mining problem in the text domains. The difficulty finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, document organization, and indexing. In text mining, clustering the sentence is one of the processes and used within general text mining tasks. Several clustering methods and algorithms are used...

The late detection of the kick (the entrance of underground fluids into oil wells) leads to oil well blowouts. It causes human life loss and imposes a great deal of expenses on the petroleum industry. This paper presents the application of adaptive neuro-fuzzy inference system designed for an earlier kick detection using measurable drilling parameters. In order to generate the initial fuzzy inf...

Methods for supervised and unsupervised clustering and machine learning were studied in order to automatically model relationships between gene expression data and gene functions of the microorganism Escherichia coli. From a pre-selected subset of 265 genes (belonging to 3 functional groups) the function has been predicted with an accuracy of 63-71 % by various data mining methods described in ...