Browsing by Author "Saranya, P."

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    A Critical Review on Potential Use of Waste Foundry Sandin Geotechnical and Pavement Applications
    (Springer Science and Business Media Deutschland GmbH, 2024) Basayya Balulmath, A.; Sridhar, G.; Saranya, P.
    In recent years, industrial recycled and waste materials have been utilized considerably in various civil engineering applications. To aid the metal casting process, metal foundries throughout the world use about 105 million tons of foundry sand annually. When the sand becomes unfit for molding, it is discarded in the landfill as waste foundry sand (WFS). India produces around 3 million tons of foundry sand annually. US Environmental protection Agency (EPA) has estimated that applications of WFS in construction works could prevent 20,000 tons of CO2 emissions and save 200 billion BTU of energy. Sustainable reuse of WFS can furnish an economical and environmentally beneficial solution to conserve landfills and virgin sands. This paper presents a state-of-the-art review of the reuse potentials and engineering properties of WFS as a suitable material in various geotechnical and pavement applications. This study discusses available information on WFS from a geotechnical perspective. Evaluation and characterization of geotechnical behavior and environmental properties of WFS may necessitate its effective utilization in the construction industry. Some existing recovery processes of WFS and its uses are also discussed. Large-scale application of WFS in various civil engineering works may significantly reduce the quantity of waste generated in the state. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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    Autism Spectrum Disorder Detection Using Machine Learning Techniques
    (Springer Science and Business Media Deutschland GmbH, 2025) Shetty, S.; Shetty, S.; Saranya, P.
    A developmental disease called autism spectrum disorder (ASD) greatly reduces a patient's capacity for social interaction and communication in everyday situations. Using various machine learning strategies, necessitating (KNN)-K Nearest Neighbors, (LR)-Logistic Regression, (DT)-Decision Tree Classifier, (RF)-Random Forest Classifier, and (SVM)-Support Vector Machine. Using data taken from potential ASD patients’ medical records, a strong machine learning-driven strategy for autism detection is established. The ASD dataset, which is accessible to the public, is used to assess the suggested methods. There are 800 cases and 22 distinct attributes in the ASD screening dataset. The framework involves data collection, data visualization, data preprocessing, and implementation of machine learning model. A machine learning-based ASD detection system using logistic regression aims to determine if an individual has ASD or not in accordance with relevant features and behavioral patterns. Testing results are evaluated based on the performance metrics and the proposed system utilizes Logistic Regression which yields 0.85 accuracy, 0.78 precision, 0.76 recall, and 0.77 F1 score following comparison with the other models of machine learning. The suggested framework for ASD detection greatly streamlines and expedites the process of diagnosing ASDs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.