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This paper delves into the training and testing of two pre-trained models of a Convolutional Neural Network (CNN) to classify images of clothing from the Fashion MNIST dataset and determine the classification accuracy and performance of both the models. The Fashion MNIST dataset is a collection of 60,000 28x28 grayscale images of 10 different types of clothing. The images are well-labeled and comparatively easy to classify, making them a good starting point for learning about CNNs. The article starts with a summary of the Fashion MNIST dataset. There are 60,000 training photos and ten thousand test ones in the dataset. T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag and Ankle boot are the pictures' ten constituent classes. The CNN architecture used in the tests is described in the next part of the study. CNN architecture is made up of two convolutional layers, two max-pooling layers, and two fully linked layers. The convolutional layers employ 32 3x3 filters, whereas the max pooling layers employ a pool size of 2x2. There are 128 neurons in the fully linked layer and 10 neurons in the unconnected layer. The Adam optimizer is chosen to train the CNN framework utilizing the learning rate of 1e-5. The machine learning algorithm underwent training for 30 epochs with a batch size of 64. The experiments' shortcomings are highlighted in the conclusion section. It is certainly possible that the CNN model would not perform as well on other datasets. Another limitation is that the experiments were conducted only for a set of hyperparameters. The outcomes of the experiments are presented in the results section. The training accuracy of the Convolutional Neural Network models VGG19 and Resnet50 are 94.58 and 99.41 respectively. The testing accuracies for VGG19 and Resnet50 models are 91.92 and 90.25 respectively. The inference latencies for both cases were found to be 5.439 and 3.522 respectively. [ABSTRACT FROM AUTHOR]

This investigation focuses on the environmental impacts of cement production as well as possible mitigation strategies for global warming. Limestone and other nonrenewable resources have become less plentiful as a result of increased rawmaterial gathering to meet the rising demand for cement. In addition to harming the natural habitat of several plant and animal species, resource extraction from natural ecosystems increases the possibility of ecological imbalance. Due to its rapid consumption, the likelihood that this finite resource will run out in the near future is rising quickly. The company'sraw material processing stages also generate a lot of dust, noise, and greenhouse gases, especially carbon dioxide, all of which degrade the environment and hasten climate change. People's daily life are made harder by unwelcome environmental impediments. One way to address this issue is through improved cement plant production techniques, which enable the creation of cleaner cement. Industrial waste could be utilised in cement-free concrete or as anadditional cementitious material, lowering the nation's dependency on cement and improving the environment. We need to employ materials that use less natural resources, are more economical, and have less of an impact on the environment if we hope to be successful in using other alternative materials as concrete binder. In the end, this will contribute to a more robust and wholesome ecosystem for coming generations. [ABSTRACT FROM AUTHOR]

In this paper, we study the propagation of Rayleigh-type wave in a heterogeneous isotropic elastic layer with initial stress resting on a rigid foundation. Frequency equation is obtained in closed form. The frequency equation being a function of phase velocity, wave number, initial stress and heterogeneous parameter associated with the rigidity and density of inhomogeneous layer reveals the fact that Rayleigh-type wave propagation is greatly influenced by above stated parameters. In Numerical and graphical computation, the significant effects of distortional velocity have been carried out. Moreover, the obtained dispersion relation is found in well-agreement to the classical case in homogeneous isotropic layer resting on a rigid foundation. [ABSTRACT FROM AUTHOR]