Any disease is curable if it is diagnosed at the early stages with the help of a little human effort. The disease breast cancer is the second leading cause of death among women after lung cancer. Mammography is one of the most mainstream clinical imaging modalities that are utilized for early recognition of breast cancer. Early breast cancer detection helps to alleviate unnecessary treatments as well as saving women’s lives. The speedy development in deep learning and some of the strategies of machine learning have invigorated abundant enthusiasm for their application to clinical imaging issues. This paper presents an improved convolutional neural network (CNN) model that consists of three convolutional layers where the starting layer searches for low-level features and the ending layer searches for high-level features. Two activation functions, that is, the Rectified Linear Unit and sigmoid functions, are utilized for the detection of breast cancer using digitized film mammograms from the Digital Database for Screening Mammography. The proposed convolutional neural system for identifying breast malignancy on mammogram imaging achieved praiseworthy execution on examination with prior models. The experimentation found that the model achieved has a true positive rate of 99% (accuracy = 97.20%, precision = 99%, true negative rate = 96%, F-score = 0.99, balanced classification rate = 0.975, Youden’s index = 0.95). The proposed improved CNN model can be used as a second opinion of doctors to detect breast cancer.

No abstract

A signifi cant attention is drawn by researchers in recent years towards cements with partial replacements of supplementary cementitious materials [SCM], to reduce the adverse effect of global warming. The research presented in this paper involves the microstructure study of composite cement [CC] which falls into the category of ternary blended cements, consisting of ordinary Portland cement [OPC] and SCMs: Fly Ash [FA] and Granulated Blast Furnace Slag [GBFS], as partial replacement of OPC. The proportion of OPC, FA, and GBFS considered are 45%, 22%, and 33% respectively which is in accordance with IS 16415: 2015. The effi cacy of using the CC effectively in construction practice needs the understanding of the behaviour and phase transforma-tions occurring during the process of hydration, which affects the strength and performance of mortars. This study examines the phase changes at different curing periods viz. 3,7,14,21,28,56 and 90 days, along with the strength of OPC and CC mortars as well as the microstructure investigation. As it was expected, the initial rate of strength gain of CC is lower, compared to OPC, due to the slow development of pozzolanic activity. The strength gain of OPC has practically obtained its class at 28 days but in CC it has continued till 90 days. The ultimate strengths of both the cements are quite comparable at the end of 90 days.

No abstract

No abstract