Faculty Publications
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Item Neuro-fuzzy based approach for wave transmission prediction of horizontally interlaced multilayer moored floating pipe breakwater(2011) Patil, S.G.; Mandal, S.; Hegde, A.V.; Alavandar, S.The ocean wave system in nature is very complicated and physical model studies on floating breakwaters are expensive and time consuming. Till now, there has not been available a simple mathematical model to predict the wave transmission through floating breakwaters by considering all the boundary conditions. This is due to complexity and vagueness associated with many of the governing variables and their effects on the performance of breakwater. In the present paper, Adaptive Neuro-Fuzzy Inference System (ANFIS), an implementation of a representative fuzzy inference system using a back-propagation neural network-like structure, with limited mathematical representation of the system, is developed. An ANFIS is trained on the data set obtained from experimental wave transmission of horizontally interlaced multilayer moored floating pipe breakwater using regular wave flume at Marine Structure Laboratory, National Institute of Technology Karnataka, Surathkal, India. Computer simulations conducted on this data shows the effectiveness of the approach in terms of statistical measures, such as correlation coefficient, root-mean-square error and scatter index. Influence of input parameters is assessed using the principal component analysis. Also results of ANFIS models are compared with that of artificial neural network models. © 2010 Elsevier Ltd. All rights reserved.Item Nonlinear system identification using memetic differential evolution trained neural networks(2011) Subudhi, B.; Jena, D.Several gradient-based approaches such as back propagation (BP) and Levenberg Marquardt (LM) methods have been developed for training the neural network (NN) based systems. But, for multimodal cost functions these procedures may lead to local minima, therefore, the evolutionary algorithms (EAs) based procedures are considered as promising alternatives. In this paper we focus on a memetic algorithm based approach for training the multilayer perceptron NN applied to nonlinear system identification. The proposed memetic algorithm is an alternative to gradient search methods, such as back-propagation and back-propagation with momentum which has inherent limitations of many local optima. Here we have proposed the identification of a nonlinear system using memetic differential evolution (DE) algorithm and compared the results with other six algorithms such as Back-propagation (BP), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm Back-propagation (GABP), Particle Swarm Optimization combined with Back-propagation (PSOBP). In the proposed system identification scheme, we have exploited DE to be hybridized with the back propagation algorithm, i.e. differential evolution back-propagation (DEBP) where the local search BP algorithm is used as an operator to DE. These algorithms have been tested on a standard benchmark problem for nonlinear system identification to prove their efficacy. First examples shows the comparison of different algorithms which proves that the proposed DEBP is having better identification capability in comparison to other. In example 2 good behavior of the identification method is tested on an one degree of freedom (1DOF) experimental aerodynamic test rig, a twin rotor multi-input-multi-output system (TRMS), finally it is applied to Box and Jenkins Gas furnace benchmark identification problem and its efficacy has been tested through correlation analysis. © 2011 Elsevier B.V.Item Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor(Elsevier Ltd, 2016) Nair, V.V.; Dhar, H.; Kumar, S.; Thalla, A.K.; Mukherjee, S.; Wong, J.W.C.The performance of a laboratory-scale anaerobic bioreactor was investigated in the present study to determine methane (CH4) content in biogas yield from digestion of organic fraction of municipal solid waste (OFMSW). OFMSW consists of food waste, vegetable waste and yard trimming. An organic loading between 40 and 120 kg VS/m3 was applied in different runs of the bioreactor. The study was aimed to focus on the effects of various factors, such as pH, moisture content (MC), total volatile solids (TVS), volatile fatty acids (VFAs), and CH4 fraction on biogas production. OFMSW witnessed high CH4 yield as 346.65 L CH4/kg VS added. A target of 60–70% of CH4 fraction in biogas was set as an optimized condition. The experimental results were statistically optimized by application of ANN model using free forward back propagation in MATLAB environment. © 2016 Elsevier LtdItem Dew Point temperature estimation: Application of artificial intelligence model integrated with nature-inspired optimization algorithms(MDPI AG indexing@mdpi.com Postfach Basel CH-4005, 2019) Naganna, S.R.; Deka, P.C.; Ghorbani, M.A.; Biazar, S.M.; Al-Ansari, N.; Yaseen, Z.M.Dew point temperature (DPT) is known to fluctuate in space and time regardless of the climatic zone considered. The accurate estimation of the DPT is highly significant for various applications of hydro and agro-climatological researches. The current research investigated the hybridization of a multilayer perceptron (MLP) neural network with nature-inspired optimization algorithms (i.e., gravitational search (GSA) and firefly (FFA)) to model the DPT of two climatically contrasted (humid and semi-arid) regions in India. Daily time scale measured weather information, such as wet bulb temperature (WBT), vapor pressure (VP), relative humidity (RH), and dew point temperature, was used to build the proposed predictive models. The efficiencies of the proposed hybrid MLP networks (MLP-FFA and MLP-GSA) were authenticated against standard MLP tuned by a Levenberg-Marquardt back-propagation algorithm, extreme learning machine (ELM), and support vector machine (SVM) models. Statistical evaluation metrics such as Nash Sutcliffe efficiency (NSE), root mean square error (RMSE), and mean absolute error (MAE) were used to validate the model efficiency. The proposed hybrid MLP models exhibited excellent estimation accuracy. The hybridization of MLP with nature-inspired optimization algorithms boosted the estimation accuracy that is clearly owing to the tuning robustness. In general, the applied methodology showed very convincing results for both inspected climate zones. © 2019 by the authors.Item Semi-supervised structure attentive temporal mixup coherence for medical image segmentation(Elsevier B.V., 2022) Pawan, S.J.; Jeevan, G.; Rajan, J.Deep convolutional neural networks have shown eminent performance in medical image segmentation in supervised learning. However, this success is predicated on the availability of large volumes of pixel-level labeled data, making these approaches impractical when labeled data is scarce. On the other hand, semi-supervised learning utilizes pertinent information from unlabeled data along with minimal labeled data, alleviating the demand for labeled data. In this paper, we leverage the mixup-based risk minimization operator in a student–teacher-based semi-supervised paradigm along with structure-aware constraints to enforce consistency coherence among the student predictions for unlabeled samples and the teacher predictions for the corresponding mixup sample by significantly diminishing the need for labeled data. Besides, due to the intrinsic simplicity of the linear combination operation used for generating mixup samples, the proposed method stands at a computational advantage over existing consistency regularization-based SSL methods. We experimentally validate the performance of the proposed model on two public benchmark datasets, namely the Left Atrial (LA) and Automatic Cardiac Diagnosis Challenge (ACDC) datasets. Notably, on the LA dataset's lowest labeled data set-up (5%), the proposed method significantly improved the Dice Similarity Coefficient and the Jaccard Similarity Coefficient by 1.08% and 1.46%, respectively. Furthermore, we demonstrate the efficacy of the proposed method with a consistent improvement across various labeled data proportions on the aforementioned datasets. © 2022 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of SciencesItem Predicting Burden Rock Velocity in Limestone Mines using Artificial Neural Network Models(World Researchers Associations, 2025) Channabassamma, N.; Akhil, A.; Rama, S.V.; Sahas, S.V.; Ranjit, K.The prediction of burden rock velocity is crucial in optimizing the efficiency of mining and excavation operations. This study presents a novel approach utilizing Artificial Neural Networks (ANNs) to accurately predict the velocity of burden rocks based on various input parameters such as rock property, geological property and bench properties. A comprehensive dataset was collected from field measurements and laboratory experiments to train the ANN models. The performance of the ANN models such as Multi-layered Perceptron (MLP), Deep Neural Network (DNN), simple MLP and Backpropagation Neural Network (BPNN) was evaluated based on performance metrics R-squared (R)2, Mean Squared Error (MSE) and Mean Absolute Error (MAE). Among the developed ANN models, the BPNN model was found to be the most accurate predictive model for burden rock velocity, as evidenced by metrics R2(0.821), MSE (0.099) and MAE (0.226). The results indicate that the BPNN model effectively captures the complex relationships between the predictors and burden rock velocity. Advanced neural network algorithms such as recurrent neural networks and long short-term memory techniques can be used to improve the accuracy of presented neural network models. © 2025, World Researchers Associations. All rights reserved.