Daily Archives: September 27, 2024

Abstract-The construction industry faces increasing pressure to adopt sustainable practices due to resource depletion and waste management challenges. This review critically examines the use of Portland Slag Cement (PSC) in combination with Recycled Aggregate Concrete (RAC) to enhance sustainability and performance in construction. The analysis consolidates research on the mechanical properties, durability, and environmental impact of PSC-RAC composites. Findings show that PSC enhances compressive strength, tensile strength, and long-term durability while reducing the carbon footprint of concrete production. The review highlights the superior performance of PSC in comparison to traditional cementitious materials, particularly in harsh environments. However, challenges remain regarding the variability in the quality of recycled aggregates, workability issues, and economic feasibility. This review emphasizes the need for standardized quality controls for recycled materials and advocates for further research into long-term performance and the integration of PSC with advanced materials such as Nano-Silica. Comprehensive studies and cost-benefit analyses are recommended to fully explore the feasibility of PSC-RAC in both structural and non-structural applications.

Abstract-Banking system vulnerabilities have made us vulnerable to fraudulent activities that seriously harm the bank’s reputation and financial standing in addition to harming clients. An estimated large sum of money is lost financially each year as a result of financial fraud in banks. Early discovery aids in the mitigation of the fraud by allowing for the development of a countermeasure and the recovery of such losses. This research proposes a machine learning-based method to effectively aid in fraud detection. In order to combat counterfeits and minimise damage, the artificial intelligence (AI) based model will expedite the check verification process. In order to determine the association between specific parameters and fraudulence, we examined a number of clever algorithms that were trained on a public dataset in this article.

Abstract-A serious and pervasive lung disease with a poor diagnosis rate is tuberculosis. Following the vacuity of high-resolution coffin x-rays, deep literacy can now yield results for the successful discovery of this unpleasant complaint and other possible operations in the health sector. This study presents a new deep learning algorithm for tuberculosis identification using a coffin x-ray image bracket to acquire geographical data. It combines the ImageNet dataset with two popular, trained vgg16 and vgg19 models. The system that is being described is validated through trials using the chest x-ray dataset. After assessing the model on the test set, we receive a score of 0.9992 for each of the criteria (delicacy, perfection, recall, and f1-score).