Investigation of Forced Convection Enhancement and Entropy Generation of Nanofluid Flow through a Corrugated Minichannel Filled with a Porous Media.

Corrugating channel wall is considered to be an efficient procedure for achieving improved heat transfer. Further enhancement can be obtained through the utilization of nanofluids and porous media with high thermal conductivity. This paper presents the effect of geometrical parameters for the determination of an appropriate configuration. Furthermore, the optimization of forced convective heat transfer and fluid/nanofluid flow through a sinusoidal wavy-channel inside a porous medium is performed through the optimization of entropy generation. The fluid flow in porous media is considered to be laminar and Darcy-Brinkman-Forchheimer model has been utilized. The obtained results were compared with the corresponding numerical data in order to ensure the accuracy and reliability of the numerical procedure. As a result, increasing the Darcy number leads to the increased portion of thermal entropy generation as well as the decreased portion of frictional entropy generation in all configurations. Moreover, configuration with wavelength of 10 mm, amplitude of 0.5 mm and phase shift of 60° was selected as an optimum geometry for further investigations on the addition of nanoparticles. Additionally, increasing trend of average Nusselt number and friction factor, besides the decreasing trend of performance evaluation criteria (PEC) index, were inferred by increasing the volume fraction of the nanofluid (Al2O3 and CuO).

Employing the Local Radon Transform for Melanoma Segmentation in Dermoscopic Images.

In recent years, the number of patients suffering from melanoma, as the deadliest type of skin cancer, has grown significantly in the world. The most common technique to observe and diagnosis of such cancer is the use of noninvasive dermoscope lens. Since this approach is based on the expert ocular inference, early stage of melanoma diagnosis is a difficult task for dermatologist. The main purpose of this article is to introduce an efficient algorithm to analyze the dermoscopic images. The proposed algorithm consists of four stages including converting the image color space from the RGB to CIE, adjusting the color space by applying the combined histogram equalization and the Otsu thresholding-based approach, border extraction of the lesion through the local Radon transform, and recognizing the melanoma and nonmelanoma lesions employing the ABCD rule. Simulation results in the designed user-friendly software package environment confirmed that the proposed algorithm has the higher quantities of accuracy, sensitivity, and approximation correlation in comparison with the other state-of-the-art methods. These values are obtained 98.81 (98.92), 94.85 (89.51), and 90.99 (86.06) for melanoma (nonmelanoma) lesions, respectively.

Feature subset selection using constrained binary/integer biogeography-based optimization.

Feature selection plays a crucial role in applications where data consists of hundreds of features due to curse of dimensionality. This paper presents two feature selection methods by modifying the main operators of Biogeography-Based Optimization algorithm. The difference between these methods is in employing binary or integer coding. The simulations perform on datasets with different feature dimensions and classes. The results indicate the effectiveness of the proposed methods in comparison with other most frequently used meta-heuristic strategies in feature selection problems.