19c96fd51791d8d23b93b93 20211006081813938 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 10 30 2021 10 12 10.35940/ijitee.10.12 https://www.ijitee.org/download/volume-10-issue-12/ M.Tech. Student, Department of Mechanical Engineering, SRES’s Sanjivani College of Engineering, Kopargoan, Affiliated to Savitribai Phule Pune University, Pune, (Maharashtra), India. Ashish B. Pendharkar Laxmikant S. Dhamande Associate Professor, SRES’s Sanjivani College of Engineering, Kopargoan, affiliated to Savitribai Phule Pune University, Pune, (Maharashtra), India. In sugar industry, the sugar processing done in different sections, but to increase total crushing per day (TCD) capacity, the milling section takes a vital role in the sugar industry. The sugar industry aims to extract the maximum amount of juice from sugarcane. In the milling section, the processed sugarcane is fed in between the three-roller shaft from the different arrangements, there are different loads applied on each part roller shafts. When load between all rollers varies then there is a chance of bending it is analyzed to check the roller shaft condition. The modeling is done on roller shaft with the help of CATIA V5. After modeling, we analyze the condition of the rollers, when different stress or forces are applied to different sections of the roller shaft it analyzed with the help of Finite element method using ANSYS WORKBENCH software. We were selecting titanium alloy materials for the roller shaft to analyze the variation in results. When comparing the calculated and software-based results using Maximum Shear stress and Total deformation for top, feed, and discharge rollers said the roller shafts are safe to use in the sugar industry and titanium alloy is the best material for these roller shafts. 10 30 2021 1 6 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.J9417.10101221 https://www.ijitee.org/wp-content/uploads/papers/v10i12/J941708101021.pdf Department of Computer Science and Engineering, GITAM Institute of Technology, GITAM Deemed to be University, Visakhapatnam, India. Karthika Gidijala Mansa Devi Pappu Department of Computer Science and Engineering, GITAM Institute of Technology, GITAM Deemed to be University, Visakhapatnam, India. Manasa Vavilapalli Department of Computer Science and Engineering, Dadi Institute of Engineering and Technology, Visakhapatnam, India. Mahesh Kothuru Department of Computer Science and Engineering, GITAM Institute of Technology, GITAM Deemed University, Visakhapatnam, India. Many different models of Convolution Neural Networks exist in the Deep Learning studies. The application and prudence of the algorithms is known only when they are implemented with strong datasets. The histopathological images of breast cancer are considered as to have much number of haphazard structures and textures. Dealing with such images is a challenging issue in deep learning. Working on wet labs and in coherence to the results many research have blogged with novel annotations in the research. In this paper, we are presenting a model that can work efficiently on the raw images with different resolutions and alleviating with the problems of the presence of the structures and textures. The proposed model achieves considerably good results useful for decision making in cancer diagnosis. 10 30 2021 7 12 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.L9524.10101221 https://www.ijitee.org/wp-content/uploads/papers/v10i12/L952410101221.pdf