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Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28507
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Item Accelerating MCMC using model reduction for the estimation of boundary properties within Bayesian framework(Pleiades journals, 2019) Gnanasekaran, N.; Kumar, M.K.In this work, Artificial Neural Network (ANN) and Approximation Error Model (AEM) are proposed as model reduction methods for the simultaneous estimation of the convective heat transfer coefficient and the heat flux from a mild steel fin subject to natural convection heat transfer. The complete model comprises of a three-dimensional conjugate heat transfer from fin whereas the reduced model is simplified to a pure conduction model. On the other hand, the complete model is then replaced with ANN model that acts as a fast forward model. The modeling error that arises due to reduced model is statistically compensated using Approximation Error Model. The estimation of the unknown parameters is then accomplished using the Bayesian framework with Gaussian prior. The sampling space for both the parameters is successfully explored based on Markov chain Monte Carlo method. In addition, the convergence of the Markov chain is ensured using Metropolis–Hastings algorithm. Simulated measurements are used to demonstrate the proposed concept for proving the robustness; finally, the measured temperatures based on in-house experimental setup are then used in the inverse estimation of the heat flux and the heat transfer coefficient for the purpose of validation. © Springer Nature Singapore Pte Ltd. 2019.Item Beyond the data range approach to soft compute the reflection coefficient for emerged perforated semicircular breakwater(Springer, 2019) Kundapura, S.; Hegde, A.V.; Wazerkar, A.V.Prediction of reflection coefficient (Kr) for emerged perforated semicircular breakwater (EPSBW) using artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) is carried out in the present paper. A new approach has been adopted in the present work using ANN and ANFIS models for the prediction of the reflection coefficient (Kr) for the wave periods beyond the range of the dataset used for training the network. The experimental data obtained for a scaled down EPSBW model from regular wave flume experiments at Marine Structure laboratory of National Institute of Technology Karnataka, Surathkal, Mangaluru, India was used. The ensemble was segregated such that certain higher ranges of wave periods were excluded in the training, and possibility of prediction was checked. The independent input parameters (Hi, T, S, D, R, d, hs) that influence the reflection coefficient (Kr) are considered for training as well as testing, where Hi is the incident wave height, T is the wave period, S is the spacing of perforations, D is the diameter of the perforations, R is the radius of the breakwater, d is the depth of the water and hs is the structure height. The accuracy of predictions of reflection coefficient (Kr) is done based on the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The study shows that ANN and ANFIS models may be used for prediction of reflection coefficient Kr of semicircular breakwater for beyond the data range of wave periods used for training. However, ANFIS outperformed ANN model in the prediction of Kr in the case of beyond the data range segregation method. © Springer Nature Singapore Pte Ltd. 2019.Item Stability Analysis of Emerged Seaside Perforated Quarter Circle Breakwater Using Soft Computing Techniques(Springer Science and Business Media Deutschland GmbH, 2022) Madhusoodhanan, S.; Rao, S.Breakwaters are constructed to address a variety of coastal requirements ranging from maintaining tranquility conditions for a port or harbor area to prevent coastal recession. Quarter circle breakwater (QCB) is a composite structure, with a rubble mound foundation and a super structure comprising of a quarter circular surface facing incident waves resting on a horizontal bottom with a rear vertical wall alongside. Be it any structure, it is essential that the design is economic, safe, and functional. Thus, the accurate estimation of minimum (critical) weight of the super structure required to oppose the sliding is vital. Also, physical model studies can be laborious and time-consuming, whereas numerical modeling can be complex. Therefore, under such circumstances, soft computing techniques prove to be handy if sufficient data are available. In this study, W/γHi 2 of an emerged seaside perforated QCB for varying S/D ratios is estimated using ANN, SVM, and AdaBoost models. Hi/gT2, d/hs, and p (%) are chosen as input parameters with the W/γHi 2 as the output parameter. Further, the obtained results are compared using performance indicators such as RMSE, R2, and MAE following which the best model is selected. The data that are used for the present study is collected from the laboratory investigation conducted in the Wave Mechanics Laboratory of the Department of Water Resources and Ocean Engineering, National Institute of Technology Karnataka, Surathkal. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Enhancing Cybersecurity: Malicious Webpage Detection Using Machine and Deep Learning(Springer Science and Business Media Deutschland GmbH, 2025) Madhusudhan, R.; Surashe, S.V.; Pravisha, P.A wide range of techniques have been proposed for detecting malicious webpages; however, with the advent of more sophisticated webpage creation processes, it has become more challenging for these approaches to deliver satisfactory outcomes. Blacklisting and classification techniques were used in the past to identify malicious webpages. The classification of the websites becomes more challenging if they are not included on the blacklist. Machine learning techniques are gaining popularity in cybersecurity. One disadvantage of the machine learning model is that it becomes slower when using content-based features. While getting the whois feature, which gives creation, updation, and expiration dates of the webpage, the webpage is physically visited. Hence, there is a chance of malicious activity. Therefore, the process of feature extraction becomes challenging and time-consuming. This article uses the Term Frequency-Inverse Document Frequency (TF-IDF) and Natural Language Processing (NLP) methods to obtain the corpus for benign and malicious words present in the Unified Resource Locator (URL). An artificial neural network (ANN) has been employed to categorize websites as benign or malicious. A comparative analysis of artificial neural networks (ANN) with other machine learning approaches has been conducted. The experimental results demonstrate that ANN has the highest accuracy of 96.70%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.