Faculty Publications
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Publications by NITK Faculty
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Item Robust infrared target tracking using discriminative and generative approaches(Elsevier B.V., 2017) Asha, C.S.; Narasimhadhan, A.V.The process of designing an efficient tracker for thermal infrared imagery is one of the most challenging tasks in computer vision. Although a lot of advancement has been achieved in RGB videos over the decades, textureless and colorless properties of objects in thermal imagery pose hard constraints in the design of an efficient tracker. Tracking of an object using a single feature or a technique often fails to achieve greater accuracy. Here, we propose an effective method to track an object in infrared imagery based on a combination of discriminative and generative approaches. The discriminative technique makes use of two complementary methods such as kernelized correlation filter with spatial feature and AdaBoost classifier with pixel intesity features to operate in parallel. After obtaining optimized locations through discriminative approaches, the generative technique is applied to determine the best target location using a linear search method. Unlike the baseline algorithms, the proposed method estimates the scale of the target by Lucas-Kanade homography estimation. To evaluate the proposed method, extensive experiments are conducted on 17 challenging infrared image sequences obtained from LTIR dataset and a significant improvement of mean distance precision and mean overlap precision is accomplished as compared with the existing trackers. Further, a quantitative and qualitative assessment of the proposed approach with the state-of-the-art trackers is illustrated to clearly demonstrate an overall increase in performance. © 2017 Elsevier B.V.Item A Boosting-Based Hybrid Feature Selection and Multi-Layer Stacked Ensemble Learning Model to Detect Phishing Websites(Institute of Electrical and Electronics Engineers Inc., 2023) Lakshmana Rao, L.R.; Rao, R.S.; Pais, A.R.; Gabralla, L.A.Phishing is a type of online scam where the attacker tries to trick you into giving away your personal information, such as passwords or credit card details, by posing as a trustworthy entity like a bank, email provider, or social media site. These attacks have been around for a long time and unfortunately, they continue to be a common threat. In this paper, we propose a boosting based multi layer stacked ensemble learning model that uses hybrid feature selection technique to select the relevant features for the classification. The dataset with selected features are sent to various classifiers at different layers where the predictions of lower layers are fed as input to the upper layers for the phishing detection. From the experimental analysis, it is observed that the proposed model achieved an accuracy ranging from 96.16 to 98.95% without feature selection across different datasets and also achieved an accuracy ranging from 96.18 to 98.80% with feature selection. The proposed model is compared with baseline models and it has outperformed the existing models with a significant difference. © 2013 IEEE.Item The effectiveness of machine learning-based multi-model ensemble predictions of CMIP6 in Western Ghats of India(John Wiley and Sons Ltd, 2023) Shetty, S.; Umesh, P.; Shetty, A.The popularity of cutting-edge machine learning ensemble approaches has solved many climate change research and prediction issues. The six top-performing GCMs obtained from Technique for Order Preference by Similarity to an Ideal Solution were ensembled using seven machine learning ensemble methods such as Random Forest Regressor (RFR), Support Vector Regressor (SVR), Linear Regression (LR), Adaptive Boosting Regressor (AdaBoost), Extreme Gradient Boosting Regressor (XGBR), Extra Tree Regressor (ETR), Multi-Layer Perceptron neural network (MLP) and simple Arithmetic Mean (AM) over the diverse geo-climatic basins. Precipitation is best simulated by EC-Earth3 and BCC-CSM2-MR. Maximum temperature by MPI-ESM1-2-HR, EC-Earth3-Veg, INM-CM5-0 and MPI-ESM1-2-LR. Minimum temperature by INM-CM5-0 and MPI-ESM1-2-LR model. The MME of XGBR and RFR stand out for their superior performance across all six basins, with exceptional performance over the per-humid basins, while AdaBoost, SVR and the AM underperform. Examining the interseasonal variability of the simulated MMEs over the basins highlights the reliability of these MME models. The anticipated change in maximum and minimum temperature in the SSP245 and SSP585 in the future horizon corroborates the undeniable rise in temperature by all the MMEs with a dramatic change in future temperature in AM and AdaBoost in precipitation with a factor of two rises in the far future over the recent past. Though climate change is expected to increase precipitation, atmospheric stabilization over the Ghats will affect the spatiotemporal distribution of precipitation. We recommend a comprehensive testing and validation approach to generate ensembles in regional investigations involving complicated and diverse precipitation mechanisms. © 2023 Royal Meteorological Society.Item Spatial Mapping of Flood Susceptibility Using Decision Tree-Based Machine Learning Models for the Vembanad Lake System in Kerala, India(American Society of Civil Engineers (ASCE), 2023) Kulithalai Shiyam Sundar, P.; Kundapura, S.Floods have claimed the lives of countless people and caused significant property damage in many countries, putting their livelihoods in the jeopardy. The Vembanad lake system (VLS) in Kerala, India, has faced adverse mishappening during 2018, 2019, and 2021 floods in the state due to torrential rainfall. The goal of this research is to construct effective decision tree-based machine learning models such as adaptive boosting (AdaBoost), random forest (RF), gradient boosting machines (GBMs), and extreme gradient boosting (XGBoost) for integrating data, processing, and generating flood susceptibility maps. There are 18 conditioning parameters considered, which include seven categories and 11 numerical data. These seven categorical data were converted to numerical data, bringing the total amount of input data to 61. The recursive feature elimination (RFE) was utilized as the feature selection technique, and a total of 22 layers were chosen to feed into the machine learning models to generate the flood susceptibility maps. The efficiencies of the models were evaluated using receiver operating characteristic (ROC)-area under the ROC curve (AUC), F1 score, accuracy, and kappa. According to the results, the performance of all four models demonstrated their practical application; however, XGBoost fared well in terms of the model's metrics. For the testing data set, the ROC-AUC values of XGBoost, GBM, and AdaBoost are 0.90, whereas it was 0.89 for RF. The accuracy varied significantly among the four models, with XGBoost scoring 0.92, followed by GBM (0.88), RF (0.87), and AdaBoost (0.87). As a result, this map may be utilized for early mitigation actions during future floods, as well as for land-use planners and emergency managers, assisting in the reduction of flood risk in regions prone to this hazard. © 2023 American Society of Civil Engineers.Item Development of machine learning regression models for the prediction of tensile strength of friction stir processed AA8090/SiC surface composites(Institute of Physics, 2024) Adiga, K.; Herbert, M.A.; Rao, S.S.; Shettigar, A.K.; Vasudeva, T.V.Friction Stir Processing is a state-of-the-art technology for microstructure refinement, material property enhancement, and fabrication of surface composites. Machine learning approaches have garnered significant interest as prospective models for modeling various production systems. The present work aims to develop four machine learning models, namely linear regression, support vector regression, artificial neural network and extreme gradient boosting to predict the influence of FSP parameters such as tool rotational speed, tool traverse speed and groove width on ultimate tensile strength of friction stir processed AA8090/SiC surface composites. These models were developed through Python programming and the original dataset was divided into 80% for the training phase and 20% for the testing phase. The performance of the models was evaluated by root mean squared error, mean absolute error and R2. Based on the results and graphical visualization, it was observed that the XGBoost model outperformed other models with high accuracy in predicting UTS of AA8090/SiC surface composites. © 2024 The Author(s). Published by IOP Publishing Ltd.Item Exploring and understanding the microwave-assisted pyrolysis of waste lignocellulose biomass using gradient boosting regression machine learning model(Elsevier Ltd, 2024) Sinha, S.; Sankar Rao, C.; Kumar, A.; Venkata Surya, D.; Basak, T.The production of bio-oil is a complex process influenced by various parameters. Optimizing these parameters can significantly enhance bio-oil yield, thus improving process efficiency. This study aims to develop a predictive model for bio-oil yield using the Gradient Boosting Regression (GBR) technique. It also seeks to identify the key factors affecting bio-oil yield and determine the optimal conditions for maximizing production. The GBR model was constructed using data collected from the literature. The model's performance was evaluated based on its determination coefficients for training and testing datasets. Optimization studies were conducted to identify the best conditions for bio-oil production. The GBR model demonstrated high precision, with determination coefficients of 0.983 and 0.913 for the training and testing datasets, respectively, indicating its effectiveness in predicting bio-oil yield. The optimal conditions for maximizing bio-oil yield were identified as 20 min of pyrolysis time, a temperature of 771 °C, and 524W of microwave power. The two-way PDP analysis provided valuable insights into the interactive effects of temperature with other factors, enhancing the understanding of the dynamics of the bio-oil production process. This study not only identifies the most impactful variables for bio-oil yield but also offers critical guidance for optimizing the production process. © 2024 Elsevier LtdItem Leveraging explainable AI framework for predictive modeling of products of microwave pyrolysis of lignocellulosic biomass using machine learning(Elsevier B.V., 2025) Kale, R.D.; Lenka, M.; Sankar Rao, C.The accurate prediction of biochar, bio-oil, and biogas yields in biomass pyrolysis is critical for optimizing process efficiency and sustainable biofuel production. In this study, machine learning (ML) models were developed using literature-derived data on biomass composition and pyrolysis conditions to predict product yields. A comparative analysis of multiple ML algorithms revealed that Decision Tree and Extra Trees exhibited the highest predictive accuracy, followed by Random Forest, Gradient Boosting Trees, and Extreme Gradient Boosting. LightGBM, Gaussian Process Regression, and CatBoost provided moderate performance, while AdaBoost demonstrated the lowest accuracy. To enhance interpretability, Explainable Artificial Intelligence (XAI) techniques, specifically SHAP analysis, were employed to identify key factors influencing pyrolysis yields. Temperature, ash content, fixed carbon, and moisture content were the dominant parameters governing biochar yield, whereas heating rate, reaction time, and feedstock properties such as carbon and volatile matter content significantly influenced bio-oil production. The gas yield was primarily driven by temperature, with secondary cracking mechanisms enhancing non-condensable gas formation. These insights provide a data-driven foundation for optimizing biomass pyrolysis processes, enabling targeted valorization strategies for lignocellulosic feedstocks. The integration of ML and XAI in this study establishes a transparent and interpretable modeling framework, facilitating informed decision-making for sustainable biofuel production. © 2025 Elsevier B.V.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 LtdItem Predicting synergistic effects on biofuel production from microalgae (Spirulina)/Tire Co-pyrolysis using ensemble machine learning(Elsevier B.V., 2025) Sridevi, V.; Al-Asadi, M.; Adnan Abdullah, T.; Nhat, T.; Sankar Rao, C.; Talib Hamzah, H.; Le, P.-C.This study investigates the synergistic effects of microwave-assisted catalytic co-pyrolysis (MACCP) of microalgae and waste tires (WT) under varying parameters such as catalyst weight, microwave power, and susceptor quantity. Optimal reaction conditions yielded a high-quality bio-oil with a maximum yield of 50.46 wt% with low water content, significantly reducing microwave energy consumption from 810 to 540 kJ. The co-pyrolysis of WT and microalgae enhanced denitrogenation and deoxygenation, improving the quality of the resulting bio-oil. Gas chromatography-mass spectrometry (GC-MS) analysis of bio-oil identified an increase in the complex composition of mono- and polyaromatic hydrocarbons and a decrease in oxygenated compounds. An ensemble machine learning approach has been employed to model and predict outcomes, achieving R2 values between 0.7 and 0.98. The models with the best predicted accuracy were Extreme Gradient Boosting (XGB) and Extra Trees (ET), both of which achieved an R2 of 0.98. The models were rigorously validated using the Leave-One-Out Cross-Validation technique, ensuring robust predictions with minimal bias by training on all but one observation iteratively and testing on the excluded data point. The work highlights the possible use of co-pyrolyzing microalgae and WT for sustainable, high-quality bio-oil production with lower energy consumption. It shows that machine learning can optimize MACCP procedures. © 2025 The Energy Institute