Evolutionary Techniques for the Solution of Bio-Heat Equation Arising in Human Dermal Region Model

Abstract

The proposed research work analyzes the bio-inspired problem through artificial neural networks with a feed-forward approach utilized to approximate the numerical results for singular nonlinear bio-heat equation (BHE) with boundary conditions based on four different scenarios created on the variation of environmental temperature to illustrate the effects of temperature on the human dermal region. The log-sigmoid function is used to construct the fitness function, while the optimization solvers: pattern search and genetic algorithm, are then hybridized with the active set technique, interior point technique, sequential quadratic programming for accurate and reliable results of the proposed BHE with various scenarios where the convergence of the numerical results is also analyzed. Moreover, a comparison of the proposed technique is expressed through residual error that reveals the nature of the numerical results and their efficiency. Additionally, a comprehensive statistical analysis is presented for the designed technique to better illustrate the accuracy, reliability, and efficiency of the obtained results.

An Extensive Investigation into the Use of Machine Learning Tools and Deep Neural Networks for the Recognition of Skin Cancer: Challenges, Future Directions, and a Comprehensive Review

“…A deeper look at the pigmented skin lesion can be obtained with dermoscopy, namely a non-intrusive imaging technique that is carried out with the use of a device known as a dermatoscope. With dermoscopy, the epidermis's structure of the skin can be seen, something that is not normally possible for the naked eye; see the recent articles [7,8]. Studies show that more and more practitioners are incorporating dermoscopy into their everyday work.…”