Efficient Welding Defect Detectionand Classification of Phased Array C-Scan Images Using Modified Fast Fuzzy C Means Andradial Bias Function Neural Network

In the recent past, phased array technology is one of the most important methodologies used for inspection of welding. The welding defect identification is a difficult task due to noise content and uneven illumination and contrast on phased array 2D image. Artificial Neural Network (ANN) is a recent Machine Learning (ML) technology that has been achieved a lot of attention over the recent years. The saliency feature extraction for representing image has become complex due to quality of 2D image. The proper image restoration and enhancement techniques should be applied in order to improve the quality of 2D phased array image. The 2D-Adaptive Anisotropic Diffusion Filter (2D AADF) is applied to eliminate noises such as impulse noise and speckle noise. The Adaptive Mean Adjustment-Contrast Limited Adaptive Histogram Equalization (AMA-CLAHE) is the enhancement technique that is applied to improve contrast and brightness of the phased array 2D image. The welding defect region can be exactly segmented using saliency mapping to contour boundaries of defects in welding. In this paper, a novel methodology for welding defect detection is applied based on Modified Fast Fuzzy C Means (MFFCM) clustering technique by integrating Probability Mass Function (PMF) threshold technique for higher range of efficient and accurate segmentation. The Gray Level Co-Occurrence Matrix (GLCM) and 2D Band-let Transform (2D BT) are applied to extract features on segmented image. TheRadial Bias Function Neural Network (RBFNN) classifier is one of the ANN classifier for classifying welding defects. Most of image classification techniques utilize RBFNN as they will provide great range of accuracy and precision while compared to existing techniques. The localized generation error model is implemented in RBFNN in order to minimize Mean Square Error (MSE). The efficiency and accuracy of the proposed methodology has been evaluated with the help of experimental results in terms of graphical representation and numerical analysis.

Summarize the Work Related to Prediction of Stock Market’s Option Prices with Artificial Intelligence using Standard Dataset

Forecasting and prediction are based on pattern recognition. It may be a human energy potential increase day today when he grownup a young guy, but afterward, his energy potential going downwards. So, we observed the pattern with the help of neural network models; these are radical bias function (RBP) and back-propagation (BP). Utilizing the neural network model, it also has many classification parts like a deep neural network, feedforward neural network, recurrent neural network, convolutional neural network and many more. In the forecasting or prediction, we have a large amount of data to manage. We trained the data with algorithm and here we also use the neural network models. We used optimization techniques that are inspired by biological swarm. Nowadays, lots of data generate day by day like market, medical, education, automobile, etc. we need recognition of the pattern for prediction of future expectations. That expectation of prediction very helpful and needy to gain profit of human beings. In this work, we use SOM (self-Organized Map), RBF (Radical Bias Function), DNN (Deep Neural Network) and PGO (Plant Grow Optimization). The total data point for the processing used 27500. The evaluation of the performance used standard parameters such as ET, MAE, MSE, RMSE and MI. The proposed algorithm implemented in MATLAB software. The cascaded neural network classifier is the combination of the SOM and RBF neural network models. The SOM neural network model proceeds the task of clustering and RBF neural network model used for prediction.

The biasing phenomenon

Context. We study biasing as a physical phenomenon by analysing geometrical and clustering properties of density fields of matter and galaxies. Aims. Our goal is to determine the bias function using a combination of geometrical and power spectrum analyses of simulated and real data. Methods. We apply an algorithm based on the local densities of particles, δ, to form simulated, biased models using particles with δ ≥ δ0. We calculate the bias function of model samples as functions of the particle-density limit δ0. We compare the biased models with Sloan Digital Sky Survey (SDSS) luminosity-limited samples of galaxies using the extended percolation method. We find density limits δ0 of biased models that correspond to luminosity-limited SDSS samples. Results. The power spectra of biased model samples allow estimation of the bias function b(> L) of galaxies of luminosity L. We find the estimated bias parameter of L* galaxies, b* = 1.85 ± 0.15. Conclusions. The absence of galaxy formation in low-density regions of the Universe is the dominant factor of the biasing phenomenon. The second-largest effect is the dependence of the bias function on the luminosity of galaxies. Variations in gravitational and physical processes during the formation and evolution of galaxies have the smallest influence on the bias function.

RAPID BLEEDING REGION DETECTION IN WIRELESS CAPSULE ENDOSCOPY VIDEOS

Wireless Capsule Endoscopy (WCC) is a medical imaging technique used to examine parts of the gastrointestinal tract. Computer aided detection is used to increase the speed of detection, better performance and reduce the time. Before finding the bleeding regions the edge regions are first detected and removed. Both the edge and the bleeding regions will share the same Hue value and the luminance should be same for the bleeding and the non -bleeding regions .We use a canny edge detector operator for detecting the edge regions in L channel. Canny edge detector is used to detect more edge pixels and preserve more bleeding pixels based up on canny edge algorithm. This method in edge removal algorithm includes edge detection, edge dilation and edge masking. After the removal of edges, those regions are made in to segment through super-pixel segmentation and regions are classified using Artificial Neural Network by Radial Bias Function (RBF).Â