Deep learning has surged in popularity in recent years, notably in the domains of medical image processing, medical image analysis, and bioinformatics. In this study, we offer a completely autonomous brain tumour segmentation approach based on deep neural networks (DNNs). We describe a unique CNN architecture which varies from those usually used in computer vision. The classification of tumour cells is very difficult due to their heterogeneous nature. From a visual learning and brain tumour recognition point of view, a convolutional neural network (CNN) is the most extensively used machine learning algorithm. This paper presents a CNN model along with parametric optimization approaches for analysing brain tumour magnetic resonance images. The accuracy percentage in the simulation of the above-mentioned model is exactly 100% throughout the nine runs, i.e., Taguchi’s L₉ design of experiment. This comparative analysis of all three algorithms will pique the interest of readers who are interested in applying these techniques to a variety of technical and medical challenges. In this work, the authors have tuned the parameters of the convolutional neural network approach, which is applied to the dataset of Brain MRIs to detect any portion of a tumour, through new advanced optimization techniques, i.e., SFOA, FBIA and MGA.

For this present research investigation, hybrid natural fiber reinforced Nano SiC particles composite was fabricated and their mechanical properties has been studied. Experiments have been planned as per Taguchi’s Method. After preparing the composite material by hand layup technique and then the mechanical characterizations are performed. Multi-response optimization has been carried out by employment of a recently developed method for multi-criteria decision making (MCDM), i.e. combined compromise solution (CoCoSo) method. For defining the relative significance of measured norms, pairwise comparison matrix was used. Optimal results have been verified through confirmatory experiments. Based on the experimental observations ultimate tensile strength (UTS), density and flexural strength, ANOVA result indicated that density has highest impact for a better mechanical behaviour. This can be useful in different field of application for fabrication of different structures, parts etc.