the late detection of the kick (the entrance of underground fluids into oil wells) leads to oil wellblowouts. 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 earlierkick detection using measurable drilling parameters. in order to generate the initial fuzzy infere...

Interval-valued data can find their practical applications in such situations as recording monthlyinterval temperatures at meteorological stations, daily interval stock prices, etc. The primary objectiveof the presented paper is to compare three different methods of fuzzy clustering for interval-valuedsymbolic data, i.e.: fuzzy c-means clustering, adaptive fuzzy c-means clustering a...

-This paper presents the application to the identification of coherent generators in a power system based on the fuzzy c-means clustering. In view of the conceptual appropriateness and computational simplicity, the fuzzy c-means give a fast and flexible method for clustering analysis. At first, the coherency measures are derived from the time-domain responses of generators to reveal the relatio...

Present study investigates the capabilities of six distinct machine learning techniques such as ANFIS network with fuzzy c-means (ANFIS-FCM), grid partition (ANFIS-GP), subtractive clustering (ANFIS-SC), feed-forward neural (FNN), Elman (ENN), and long short-term memory (LSTM) in one-day ahead soil temperature (ST) forecasting. For this aim, daily ST data gathered at three different depths 5 cm...

Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...

In current study, a particle swarm clustering method is suggested for clustering triangular fuzzy data. This clustering method can find fuzzy cluster centers in the proposed method, where fuzzy cluster centers contain more points from the corresponding cluster, the higher clustering accuracy. Also, triangular fuzzy numbers are utilized to demonstrate uncertain data. To compare triangular fuzzy ...

In Data mining, Fuzzy clustering algorithms have demonstrated advantage over crisp clustering algorithms in dealing with the challenges posed by large collections of vague and uncertain natural data. This paper reviews concept of fuzzy logic and fuzzy clustering. The classical fuzzy c-means algorithm is presented and its limitations are highlighted. Based on the study of the fuzzy c-means algor...

In Data mining, Fuzzy clustering algorithms have demonstrated advantage over crisp clustering algorithms in dealing with the challenges posed by large collections of vague and uncertain natural data. This paper reviews concept of fuzzy logic and fuzzy clustering. The classical fuzzy c-means algorithm is presented and its limitations are highlighted. Based on the study of the fuzzy c-means algor...

According to the standard fuzzy C-means clustering algorithm performed poor in the clustering effect during the clustering process. This paper presents an objective function optimization based on the attribute weighted and the objective function optimization. Firstly, use a little prior knowledge as the labeled sample. These calibrated samples information are used as the prior knowledge, and th...