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In this work, the Copper composites Cu-x wt. % B4C (x = 0, 5, 10, 15, 20) were fabricated for the metallurgical and mechanical property evaluation as per ASTM standards. The metallurgical characterization tests on the samples include x-ray diffraction, optical microscopy, and scanning electron microscopy with EDX. Further, pin-on-disc apparatus was used to investigate the tribological behavior of composite specimens. An SEM micrograph of the worn surface and wear debris, along with the Gwyddion software, has been used to discuss the wear mechanisms in detail. The Artificial Neural Networks (ANN) classifier model is also constructed to describe the wear behavior in more detail. The experimental results inferred that the addition of Boron carbide particles has enhanced the Copper's corrosion resistance in a 1 M HCl electrolyte solution from 30.34% to 74.2%, 75.08%, and 83.29% with B and C ions. Also, it significantly enhance the mechanical and tribological characteristics considerably. [ABSTRACT FROM AUTHOR]

A Computer-Aided Diagnostic (CAD) system that uses Artificial Neural Network (ANN) trained by drawing in the relative advantages of Differential Evolution (DE), Particle Swarm Optimization (PSO) and gradient descent based backpropagation (BP) for classifying clinical datasets is proposed. The DE algorithm with a modified best mutation operation is used to enhance the search exploration of PSO. The ANN is trained using PSO and the global best value obtained is used as a seed by the BP. Local search is performed using BP, in which the weights of the Neural Network (NN) are adjusted to obtain an optimal set of NN weights. Three benchmark clinical datasets namely, Pima Indian Diabetes, Wisconsin Breast Cancer and Cleveland Heart Disease, obtained from the University of California Irvine (UCI) machine learning repository have been used. The performance of the trained neural network classifier proposed in this work is compared with the existing gradient descent backpropagation, differential evolution with backpropagation and particle swarm optimization with gradient descent backpropagation algorithms. The experimental results show that DEGI-BP provides 85.71% accuracy for diabetes, 98.52% for breast cancer and 86.66% for heart disease datasets. This CAD system can be used by junior clinicians as an aid for medical decision support. [ABSTRACT FROM AUTHOR]