Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Deep Learning (DL)

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    Recent Advances in Urban Expansion Monitoring Through Deep Learning-Based Semantic Change Detection Techniques From Satellite Imagery
    (Institute of Electrical and Electronics Engineers Inc., 2024) Basavaraju, K.S.; Sravya, N.; Kevala, V.D.; Lal, S.
    Urban expansion monitoring is essential for understanding and managing the dynamic growth of cities. Recently, deep learning (DL)-based semantic change detection (SCD) techniques have emerged as powerful tools for accurately monitoring urban expansion using satellite imagery. This paper offers comprehensive overview of the recent advancements in urban expansion monitoring through DL-based SCD techniques. It covers various publicly available SCD datasets and assesses performance, advantages, and limitations of existing DL-based SCD architectures, categorized into three types. Furthermore, the paper discusses the challenges encountered in DL-based SCD techniques. Finally, it outlines future research directions in urban expansion monitoring using DL-based SCD techniques. © 2024 IEEE.
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    Inorganic Chemical Reaction Predictor Using Random Forest and Support Vector Machine
    (Institute of Electrical and Electronics Engineers Inc., 2025) Ramesh, G.; Sahil, M.; Palan, S.A.; Bhandary, D.; Shetty, S.S.; Poojary, K.K.; Sowjanya, N.
    The Chemical Reaction Predictor project shall use machine learning approaches to make predictions on chemical reaction effects. When a large enough group of known reactions is available, each identified set of reactants and products can be used to construct a model into which can be fed any set of reactants. It includes data acquisition and data pre-processing, feature selection of reactant properties and reaction conditions, and construction of several predictive models. The first and main goal is to dogmatically apply machine learning models such as Random Forests and Support Vector Machines to attain an accuracy of 60% or higher. Furthermore, we measure the accuracy, and other measures such as precision, recall, and F1 score to determine the efficiency of these models. Finally, while the optimal model is found and implemented, it is brought within a simple graphical user interface that enables the users to input reactants and obtain predicted products. © 2025 IEEE.