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Daily Archives: March 20, 2025

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Development of Eco-Friendly Bricks Using Industrial and Agricultural Waste

Development of Eco-Friendly Bricks Using Industrial and Agricultural Waste
Authors:-M P Iniya, K Sabarinathan, G Shanmugave Murugan, V Rishi

Abstract-One of the most important and often used building materials in masonry construction worldwide is brick. The environmental load brought on by trash deposition can be reduced by making bricks from waste materials. The purpose of this study is to assess the impact of adding trash made from rice husk ash. Samples were prepared with different percentages of cement, fly ash, lime, river sand, and rice husk ash. recycling a variety of waste materials, including as fly ash (40 – 60%), rice husk ash (15 – 20%), lime (10%), cement, and river sand (15 – 20%), for use in brick production. The dimensions of the brick specimen are 230 x 110 x 75 mm. Experiments are conducted to examine differences in properties including compressive strength, water absorption, hardness, and soundness. This review will lead to recommendations for additional research on the effects of that waste on bricks’ mechanical and physical properties. The uses of agricultural wastes as cheap and environmental-friendly construction materials are beneficial towards provision of affordable housing in developing country.

DOI: 10.61137/ijsret.vol.11.issue2.205

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Combining Data Filtration and Regression Learning for Enhancing the Forecasting of Cryptocurrencies

Combining Data Filtration and Regression Learning for Enhancing the Forecasting of Cryptocurrencies
Authors:-Neha Sunhare, Dr. Kamlesh Ahuja

Abstract-The cryptocurrency market is highly volatile and unpredictable, making traditional financial models less effective for price forecasting. Unlike stock markets, which are influenced by earnings reports and economic indicators, cryptocurrency prices are driven by a combination of market sentiment, technological developments, regulatory changes, and supply-demand dynamics. Due to the complexity and non-linearity of these factors, machine learning (ML) has emerged as a powerful tool for predicting crypto prices with greater accuracy. The proposed work employs the steepest descent based scaled back propagation algorithm along with the data pre-processing using the discrete wavelet transform (DWT) for crypto price prediction. It has been shown that the proposed system attains lesser MAPE% error compared to previously existing techniques making it a more accurate forecasting model.

DOI: 10.61137/ijsret.vol.11.issue2.204

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