Temperature is one of the exigent parameters that needs to be controlled in today's industries. Importantly this temperature control should be precise and fast. As the conventional controllers are not optimally tuned, the controller used for controlling the temperature of the electric furnace does not exhibit better performance. Its rise time and settling time is too large as well as it has a sizable amount of overshoot. This paper presents a Genetic Algorithm based PID controller to overcome the low precision, long rise time and settling time of the controller. In this algorithm, Integral of Absolute Error is taken as the object function for minimizing the error. Using this function, the algorithm engenders the optimum value of the gain parameters (K p , K i , K d) for the PID controller. It shows better control over the conventional controllers. As the overshoot, settling time, and rise time are substantially improved, it provides sharp and prompt control over the temperature. This precise and instant control of temperature has a great impact on the food and medicine industries. As the temperature could be controlled precisely and instantly, we can avoid the change/degradation of the physical properties of the materials that are under process.

Melanoma is the deadliest type of skin cancer. It has been rising exponentially for the last few decades. If it is diagnosed and treated at its early stage, the survival rate is very high. To prevent the invasive biopsy technique, automated diagnosis of melanoma from dermoscopy images has become a hot research area for the last few decades. This paper proposes three new distinct and effective features with some existing features related to shape, size and color properties of dermoscopy images based on ABCD rule for melanoma detection. ABCD stands for Asymmetry, Border, Color, and Diameter of the skin lesion. A two-stage segmentation approach including Otsu algorithm and Chan-Vese algorithm for lesion segmentation is implemented in this paper. Dull-Razor algorithm removes the black and dark hair from the input images and artificial neural network classifier classifies the malignant and benign images based on the extracted features. Implementation result of the proposed approach achieves 98.2% overall classification accuracy with 98% sensitivity and 98.2% specificity. These promising results indicate that the proposed system is able to assist the dermatologists in early detection of melanoma.

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