No abstract

No abstract

No abstract

In the present scenario skin cancer is found highly risk in human beings. Many forms of skin cancer are affecting the human life. Among the form of skin cancer the unpredictable diseases is Melanoma cancer. Skin cancer the fatal form is primarily diagnosed visually leads to death, if not diagnosed in its early stage. It can be identified by tedious lab testing with more time and cost. There are vast numbers of computational techniques helpful to predict diseases. A challenging task in skin lesion classification is due to the smooth variation, in the appearance of skin lesions. Image processing techniques like segmentation is used in medical science to identify the region of significance.. This paper focuses Genetic algorithms by means of adaptive parameters (adaptive genetic algorithms, AGAs), an important and promising alternative to genetic algorithms. The extent for accurate solution and convergence speed is significantly measured by employing of crossover along with mutation from which genetic algorithms appear.

The paper investigates Passivity of Simpson 1/3 rule fuzzy neural network controller by teleoperation-based wheeled mobile robot navigation and wheel slippage. To render the obstacle avoidance for wheeled mobile robot and ensuring incident-free navigation particularly unlimited workspace and surface slippage of coordination for master robot position and slave robot velocity, a passivity controller mode is employed. Effective control strategies are formulated to achieve system stability in which soft-computing methodology is accessed to bypass robot wheel slippage and skidding. Soft-computing based fuzzy neural network system is framed through Simpson 1/3 rule for reducing master/slave robot position error on behalf of unavoidable and acceptable forces, which is confirmed by teleoperation system. We concluded mathematically, the system stability which has been shown via its passivity of the fuzzy neural network controller. The forces which can be handled by the human operator are approximately equal to the forces applied by the environmental force and sensor predictor of slave robot whichever comes under the unlimited workspace. Simulation results are verified with the effect of the proposed controller.