Breast cancer (BC) is the second leading cause of death in developed and developing nations, accounting for 8% of deaths after lung cancer. Gene mutation, constant pain, size fluctuations, colour (roughness), and breast skin texture are all characteristics of BC. The University of Wisconsin Hospital donated the WDBC dataset, which was created via fine-needle aspiration (biopsies) of the breast. We have implemented multilayer perceptron (MLP), K-nearest neighbor (KNN), genetic programming (GP), and random forest (RF) on the WBCD dataset to classify the benign and malignant patients. The results show that RF has a classification accuracy of 96.24%, which outperforms all the other classifiers.

In this paper, magneto-hydrodynamic flow of four different nanoliquids is presented. Two types of nanoparticles, viz. alumina and CuO are considered in water and ethylene glycol as base fluids. Appropriate models for nanoliquid physical properties are considered to incorporate the nanoparticle aggregation effects, nanoparticle shape, and size of the nanoparticles. Similarity transformations are used to convert the partial differential equations of the flow to nonlinear ordinary differential equations. The resultant system of equations is solved by Runge–Kutta finite difference method and an error function is designed which is optimized by using a metaheuristic algorithm, namely particle swarm optimization. The effect of flow parameters, viz. mass transfer parameter and Hartmann number and the nanoliquid parameters like nature of the base liquid, nanoparticle material, nanoparticle size, concentration of nanoparticle in base liquid on velocity distributions have been analyzed and discussed. The nanoparticle concentration and the particle size are found to have a significant role in the nanoliquid flow in the channel. The numerical results obtained from the proposed numerical method are validated with the previously published work under some special cases. The proposed numerical method holds excellent potential in mathematical modeling problems where the resultant equations are nonlinear coupled ordinary differential equations with unknown initial or boundary conditions.

This chapter explores the fascinating world of hybrid nanofluids. Hybrid nanofluids are a new class of nanofluids, engineered by dispersing more than one distinct nanoparticle into conventional heat transfer fluids. The unique properties of hybrid nanofluids make them highly versatile and thus suitable for a wide range of applications. In this chapter, we will discuss the synthesis methods of hybrid nanofluids and the key factors influencing the properties of hybrid nanofluids. We also explore their diverse applications in heat transfer enhancement, energy systems, electronics cooling, biomedical fields, etc. Apart from this, understanding the potential of hybrid nanofluids will help unlock their full capabilities for addressing the challenges of the modern world. <!----> <!----> <!----> <!---->