Conference Papers

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

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Author: search.filters.author.Gangadharan, K.V.Subject: search.filters.subject.Machine Learning

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    Spatio-temporal Analysis and Modeling of Coastal areas for Water Salinity Prediction
    (Institute of Electrical and Electronics Engineers Inc., 2023) Sudhakara, B.; Priyadarshini, R.; Bhattacharjee, S.; Kamath S․, S.; Umesh, P.; Gangadharan, K.V.; Ghosh, S.K.
    Salinity is an important parameter affecting the quality of water, and excessive amounts adversely affect vege-tation growth and aquatic organism populations. Natural factors like tidal waves, natural calamities etc., and man-made factors like unchecked disposal of industrial wastes, domestic/ urban sewage, and fish hatchery activities can cause significant increases in water salinity. In this article, an approach that utilizes multimodal data like Landsat 8 optical observations and the SMAP salinity data product for predicting water salinity indices in the coastal region is proposed. Machine Learning models such as K-nearest neighbor (KNN), Gradient Boost (GB), Extremely Randomized Tree (ERT), Random Forest Regression (RFR), Decision Tree (DT), Multiple Linear Regression (MLR), Lasso Regression (LR), and Ridge Regression (RR) are used for salinity prediction. Empirical experiments revealed that the ERT model outperformed other ML models, with a R2 of 0.92 and RMSE of 0.25 psu. © 2023 IEEE.
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    Maximizing Performance and Efficiency: An Algorithm Approach to Engine Sensor Optimization using Machine Learning
    (Institute of Electrical and Electronics Engineers Inc., 2024) Varma, V.; Verma, K.; Mehta, H.; Gangadharan, K.V.
    This paper presents an algorithmic methodology developed to reduce the number of sensors required in automotive engines by leveraging machine learning techniques. The sensor data used in this technique was obtained from a standard engine, which exhibited redundancy in data. The high cost associated with sensors and their integration into engine systems necessitates an efficient approach to optimize sensor utilization while maintaining reliable engine performance. By utilizing advanced data analysis and predictive modeling, our algorithm aims to identify redundant or non-critical sensors, enabling a streamlined and cost-effective sensor configuration. We achieved this by developing a tailored dimensionality reduction algorithm based on functional dependency theory. This approach transforms data from a high-dimensional space into a lower-dimensional space, preserving essential features of the original data and ideally approximating its intrinsic dimension. © 2024 IEEE.