Faculty Publications
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Item Acoustic features based word level dialect classification using SVM and ensemble methods(Institute of Electrical and Electronics Engineers Inc., 2017) Chittaragi, N.B.; Koolagudi, S.G.In this paper, word based dialect classification system is proposed by using acoustic characteristics of the speech signal. Dialects mainly represent the different pronunciation patterns of any language. Dialectal cues can exist at various levels such as phoneme, syllable, word, sentence and phrase in an utterance. Word level dialectal traits are extracted to recognize dialects since every word exhibits significant dialect discriminating cues. Intonational Variations in English (IViE) speech corpus recorded in British English has been considered. The corpus includes nine dialects which cover nine distinct regions of British Isles. Acoustic properties such as spectral and prosodic features are derived from word level to construct the feature vector. Further, two different classification algorithms such as support vector machine (SVM) and tree-based extreme gradient boosting (XGB) ensemble algorithms are used to extract the prominent patterns that are used to discriminate the dialects. From the experiments, a better performance has been observed with word level traits using ensemble methods over the SVM classification method. © 2017 IEEE.Item Detection and Visualization of Corroded Surfaces Using Machine Learning(Springer Science and Business Media Deutschland GmbH, 2024) Shrivathsa, B.J.; Dhanya, R.; Meghana Nayak, D.; Pavan, G.S.The use of artificial intelligence in asset management greatly assists the industry and structural health monitoring systems. Using machine learning techniques for asset inspections can increase safety, reduce access costs, provide objective classification, and improve digital asset management systems. The detection and visualization of corrosion from digital images present significant advantages like automation, access to remote locations, mitigation of risk of inspectors, cost savings, and detecting speed. This paper used deep learning convolutional neural networks to build simple corrosion detection models and used an extreme gradient boosting algorithm to visualize the corroded surfaces. A large dataset of 1900 images with corrosion and without corrosion was collected using web scraping techniques and labeled accordingly. Training a deep learning model requires massive and high-resolution image datasets and intensive image labeling to approach high-level accuracy. The results and findings will improve the development of deep learning models for detecting and visualizing specific features on corroded surfaces. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item InFLuCs: Irradiance Forecasting Through Reinforcement Learning Tuned Cascaded Regressors(IEEE Computer Society, 2024) Chandrasekar, A.; Ajeya, K.; Vinatha Urundady, U.Accurate prediction of solar irradiance is essential for optimizing renewable energy sources in distributed generation systems due to its significant impact on solar power generation. Despite notable advancements, the inherent variability of irradiance presents challenges for existing models. In this article, we introduce a novel approach for irradiance forecasting using a cascaded combination of regressors applied to transformed process variables. Our method utilizes a gradient-boosted decision tree as the primary regressor to generate initial predictions, which are subsequently refined by a support vector regressor acting as an error correction module. Notably, the secondary regressor's kernel, alongside other hyperparameters, is dynamically learned through reinforcement learning with an RNN-based controller. Evaluation results demonstrate that our prediction-correction framework achieves superior performance compared to state-of-the-art approaches, as indicated by RMSE, MAE, and text{R}^{2} score metrics. Thorough comparative analysis highlights the model's enhanced accuracy and its potential for precise irradiance forecasting. © 2005-2012 IEEE.Item Predicting wave reflection coefficient of vertical caisson breakwater using machine learning: A data-driven approach(Elsevier Ltd, 2025) Shankara Krishna, A.; Rao, M.; Rao, S.Coastal zones are vital for ecological balance and human development, but are increasingly threatened by wave activity, shoreline erosion, and sea-level rise. To mitigate these challenges, engineers employ coastal protection structures. Specifically, vertical caisson breakwaters are preferred in deeper waters due to their advantages. Reflection Coefficient is an important hydrodynamic performance indicator for breakwaters. Recently, machine learning (ML) has been used for predicting coastal engineering parameters, offering an efficient means to support or augment traditional physical model studies, particularly during preliminary design phases, if sufficient quality data is available. This research focuses on using ML models to estimate the reflection coefficient of vertical caisson breakwaters based on a limited set of experimental data. Four different algorithms- Artificial Neural Network (ANN), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB)- are developed and evaluated. Hyperparameters are optimised using a hybrid approach, combining Grid Search with manual refinement. Of the four models, XGB achieved the highest prediction accuracy (Test CC = 0.9631), while Random Forest exhibited the smallest generalisation gap, indicating strong consistency across datasets. The findings from the study suggest that XGB offers an efficient tool for early-stage design applications in coastal engineering. © 2025 Elsevier Ltd