INTRODUCTION: Traveling Salesman Problem (TSP) is one of the vast research areas and has been considered as sub problems in many fields apart from computer science and also in the field of computer science.OBJECTIVES: This paper deals with the comparison of Ant System Ant System (AS) which is a variant of Ant Colony Optimization.METHODS: The performance of the Ant System is analysed by applying it on the Travelling Salesman Problem (TSP). The optimal results found on TSP using AS has been analysed with the elapsed time taken to find the optimal results, its mean, median, variance and the standard deviation.RESULTS: And also, the quality of solutions has been made by calculating the percentage of the optimality and the deviation of the solutions from the TSPLIB provides best known solutions. For instances, TSPLIB data sets have been used.CONCLUSION: Totally, 7 instances have been executed with three different set of parameters for AS and the results are analysed in terms of different parameter settings and performance metrics on each of it. The role of parameters has also been discussed along with the experimental results.

Brain Tumor identification has been regarded as a critical topic. Meanwhile, it is complicated to spot the tumor in MRI images manually from a large amount of MRI images generated is difficult and time-consuming due to unpredictable shapes and sizes of the tumor. Image Segmentation techniques make a massive impact here and help in obtaining more significant results by dividing the image into segments for prior identification of tumors. U-Net with LeakyReLu can be used for faster and precise segmentation of medical images. Thresholding is applied to identify the ROI of the tumor for better identification of the abnormality of the tumor. Identifying the tumor region from the segmented MRI image is lesser time-consuming. Therefore, our model developed using neural networks can help the doctors in precisely identifying the tumor region from the segmented images and thereby assisting them to help the patients.