Reasoning with inaccurate information is a major topic within the fields of artificial intelligence in general and knowledge representation and reasoning in particular. This thesis deals with information that can be incomplete, uncertain, and contradictory. We employ probabilistic conditional logic as a foundation for our investigation. This framework allows for the representation of uncertain ...

Commercial detection in news broadcast videos involves judicious selection of meaningful audio-visual feature combinations and efficient classifiers. And, this problem becomes much simpler if these combinations can be learned from the data. To this end, we propose an Multiple Kernel Learning based method for boosting successful kernel functions while ignoring the irrelevant ones. We adopt a int...

The efficiency and performance of the Twin Support Vector Machines (TWSVM) are better than the traditional support vector machines when it deals with the problems. However, it also has the problem of selecting kernel functions. Generally, TWSVM selects the Gaussian radial basis kernel function. Although it has a strong learning ability, its generalization ability is relatively weak. In a certai...

In this study a numerical method is developed to solve the Hammerstein integral equations. To this end the kernel has been approximated using the leastsquares approximation schemes based on Legender-Bernstein basis. The Legender polynomials are orthogonal and these properties improve the accuracy of the approximations. Also the nonlinear unknown function has been approximated by using the Berns...

This article studies the convolutional kernel function of fractal operators in bone fibers. On basis micro-nano composite structure compact bone, we abstracted physical space fibers and derived operators. The aims to construct analytical expression proves that error is core component convolution In other words, mechanics fractional controlled by function.