ENHANCING COMPRESSIVE STRENGTH PREDICTION IN SELF-COMPACTING CONCRETE USING MACHINE LEARNING AND DEEP LEARNING TECHNIQUES WITH INCORPORATION OF RICE HUSK ASH AND MARBLE POWDER

Enhancing compressive strength prediction in self-compacting concrete using machine learning and deep learning techniques with incorporation of rice husk ash and marble powder

Focusing on sustainable development, the demand for alternative materials in concrete, especially for Self-Compacting Concrete (SCC), has risen due to excessive cement usage and resulting CO2 emissions.As Compressive Strength (CS) is dominant among concrete properties, this research concentrates on developing SCC by incorporating Rice Husk Ash (RHA

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Region-Focusing Data Augmentation via Salient Region Activation and Bitplane Recombination for Target Detection

As the performance of a convolutional neural network is logarithmically proportional to the amount of training data, data augmentation has attracted increasing attention in recent years.Although the current data augmentation methods are efficient because they force the network to learn multiple parts of a given training image through occlusion or r

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Effect of honeybee products, as food supplements, on the biological activities of three Trichogramma species (Hymenoptera: Trichogrammatidae)

Abstract Egg parasitoids play a significant role in biological control for lepidopteran insects, where they kill the eggs (the first stage of the developmental cycle).Trichogramma species are the very important ones of these egg parasitoids.Under natural conditions, adult Trichogramma species are known to feed upon nectar, pollen, and honeydew.Here

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