Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
9 results
Search Results
Item Solar Irradiation Forecast Enhancement Using Hybrid Architecture(Institute of Electrical and Electronics Engineers Inc., 2023) Chiranjeevi, M.; Karlamangal, S.; Moger, T.; Jena, D.Power balancing at the grid is much more involved process due to the fact that solar power generation is primarily weather dependent, as it is relied on solar irradiation, which is very volatile and unpredictable. Accurate solar irradiation forecasting can significantly increase the performance of solar power plants. This research is motivated by the current advancements in deep learning (DL) models and its practical use in the green energy field. The proposed model combines two DL architectures: convolutional neural network (CNN) and long short-term memory (LSTM). The effectiveness of the same is analysed by comparing with recurrent neural network (RNN) family architectures. The RNN family models are Long Short Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), and Bi-directional GRU (Bi-GRU). The simulations are conducted on a publicly available data set from Desert Knowledge Australia Solar Centre (DKASC), Australia. A meteorological station across the Northern Territory (NT Solar resource) collects high resolution solar and climate data from Darwin location, which is used for the experiment. From the results, it is evident that each of the bidirectional model outperform its unidirectional equivalent architectures. However, the hybrid network (CNN-LSTM) outperforms all the individual models as per the error metric analysis. © 2023 IEEE.Item Hybrid Deep Learning-Based Potato and Tomato Leaf Disease Classification(Springer Science and Business Media Deutschland GmbH, 2024) Patil, M.A.; Manur, M.; Laxuman, C.; Parane, K.; Dodamani, B.M.; Sunkad, G.Predicting potato and tomato leaf disease is vital to global food security and economic stability. Potatoes and tomatoes are among the most important staple crops, providing essential nutrition to millions worldwide. However, many tomato and potato leaf diseases can seriously reduce food productivity and yields. We are proposing a hybrid deep learning model that combines optimized CNN (OCNN) and optimized LSTM (OLSTM). The weight values of LSTM and CNN models are optimized using the modified raindrop optimization (MRDO) algorithm and the modified shark smell optimization (MSSO) algorithm, respectively. The OCNN model is used to extract potato leaf image features and then fed into the OLSTM model, which handles data sequences and captures temporal dependencies from the extracted features. Precision, recall, F1-score, and accuracy metrics are used to analyze the output of the proposed OCNN-OLSTM model. The experimental performance is compared without optimizing the CNN-LSTM model, individual CNN and LSTM models, and existing MobileNet and ResNet50 models. The presented model results are compared with existing available work. We have received an accuracy of 99.25% potato and 99.31% for tomato. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Discrete wavelet-Ann approach in time series flow forecasting-a case study of Brahmaputra river(2012) Deka, P.C.; Haque, L.; Banhatti, A.G.This paper deals with the prediction of hydrologic behavior of the runoff for the one of the largest discharge carrier International River, Brahmaputra, located in Assam (India) at the Pandu station, by using daily time unit. The flow regime dominated by high data non-stationary and seasonal irregularity due to Himalayan climate fallout. The influence of data preprocessing through wavelet transforms has been investigated. For this, the main time series of flow data were decomposed to multi resolution time series using discrete wavelet transformations. Then these decomposed data were used as input to Artificial Neural Network (ANN) for multiple lead time flow forecasting. Various types of wavelets were used to evaluate the optimal performance of models developed. The forecasting accuracy of the models has been tested for multiple lead time upto 4 days using different decomposition levels. The performance of the proposed hybrid model has been evaluated based on the performance indices such as root mean square error (RMSE), coefficient of efficiency (CE) and mean relative error (MRE).The results shows the better forecasting accuracy by the proposed combined hybrid model over the single ANN model in hydrological time series forecasting. © 2012 CAFET-INNOVA TECHNICAL SOCIETY.Item 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 FLAG: fuzzy logic augmented game theoretic hybrid hierarchical clustering algorithm for wireless sensor networks(Springer, 2022) Naik, C.; Shetty D, P.D.Stability of the wireless sensor network (WSN) is the most critical factor in real-time and data-sensitive applications like military and surveillance systems. Many energy optimization techniques and algorithms have been proposed to extend the stability of a wireless sensor network. Clustering is a well regarded method in the research communities among them. Hence, this paper presents hybrid hierarchical artificial intelligence based clustering techniques, named FLAG and I-FLAG. The first phase of these algorithms use game-theoretic technique to elect suitable cluster heads (CHs) and later phase of the algorithms use fuzzy inference system to select appropriate super cluster heads (SCHs) among CHs. The I-FLAG is an improved version of FLAG where additional parameters like energy and distance are considered to elect CHs. Simulations are performed to check superiority of the proposed algorithms over the existing protocols like LEACH, CHEF, and CROSS. Simulation results show that the average stability period of WSN is better in FLAG and I-FLAG compared to other protocols, and so is the throughput of WSN during the stability period. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item Solar Irradiation Prediction Hybrid Framework Using Regularized Convolutional BiLSTM-Based Autoencoder Approach(Institute of Electrical and Electronics Engineers Inc., 2023) Chiranjeevi, M.; Karlamangal, S.; Moger, T.; Jena, D.Solar irradiance prediction is an essential subject in renewable energy generation. Prediction enhances the planning and management of solar installations and provides several economic benefits to energy companies. Solar irradiation, being highly volatile and unpredictable makes the forecasting task complex and difficult. To address the shortcomings of the traditional approaches, this research developed a hybrid resilient architecture for an enhanced solar irradiation forecast by employing a long short-term memory (LSTM) autoencoder, convolutional neural network (CNN), and the Bi-directional Long Short Term Memory (BiLSTM) model with grid search optimization. The suggested hybrid technique is comprised of two parts: feature encoding and dimensionality reduction using an LSTM autoencoder, followed by a regularized convolutional BiLSTM. The encoder is tasked with extracting the key features in order to deduce the input into a compact latent representation. The decoder network then predicts solar irradiance by analyzing the encoded representation's attributes. The experiments are conducted on three publicly available data sets collected from Desert Knowledge Australia Solar Centre (DKASC), National Solar Radiation Database (NSRDB), and Hawaii Space Exploration Analog and Simulation (HI-SEAS) Habitat. The analysis of univariate and multivariate-multi step ahead forecasting performed independently and it is compared with the conventional approaches. Several benchmark forecasting models and three performance metrics are utilized to validate the hybrid approach's prediction performance. The results show that the proposed architecture outperforms benchmark models in accuracy. © 2013 IEEE.Item A Hybrid Machine Learning Approach for Predicting Joint Shear Capacity in Beam-Column Connections(Springer Science and Business Media Deutschland GmbH, 2025) Sidvilasini, S.; Palanisamy, T.Accurately predicting the shear strength of beam-column connections is crucial for maintaining the structural integrity and stability of buildings, especially in seismic conditions. This study aims to address this challenge by developing and evaluating multiple machine learning regression models for estimating joint shear capacity. A dataset consisting of 445 beam-column connections with 17 key influencing variables was compiled and used to train seven distinct regression models. Among them, the four best-performing models—Quadratic Support Vector Machine (QSVM), Rational Quadratic Gaussian Process Regression (RQGPR), Kernel Ridge Regression (KRR), and Ensemble Boosting (EB)—were selected based on their predictive accuracy. To further enhance performance, these models were combined into a hybrid ensemble model, capitalizing on their complementary strengths to improve shear strength estimation. The hybrid model exhibited superior predictive performance, achieving a test RMSE of 0.0246 and an R2 value of 0.9605, significantly surpassing the accuracy of the best standalone model (RQGPR). This reinforces the advantage of ensemble learning in minimizing error and enhancing generalization. The findings of this research highlight the growing role of machine learning in structural engineering, particularly in advancing shear strength prediction methodologies. By demonstrating that a hybrid model can outperform traditional single-model approaches, this study provides valuable insights for developing safer, more resilient structures and optimizing modern engineering practices with artificial intelligence. © The Author(s), under exclusive licence to Shiraz University 2025.Item A hybrid machine learning approach for early cost estimation of pile foundations(Emerald Publishing, 2025) Deepa, G.; Niranjana, A.J.; Balu, A.S.Purpose: This study aims at proposing a hybrid model for early cost prediction of a construction project. Early cost prediction for a construction project is the basic approach to procure a project within a predefined budget. However, most of the projects routinely face the impact of cost overruns. Furthermore, conventional and manual cost computing techniques are hectic, time-consuming and error-prone. To deal with such challenges, soft computing techniques such as artificial neural networks (ANNs), fuzzy logic and genetic algorithms are applied in construction management. Each technique has its own constraints not only in terms of efficiency but also in terms of feasibility, practicability, reliability and environmental impacts. However, appropriate combination of the techniques improves the model owing to their inherent nature. Design/methodology/approach: This paper proposes a hybrid model by combining machine learning (ML) techniques with ANN to accurately predict the cost of pile foundations. The parameters contributing toward the cost of pile foundations were collected from five different projects in India. Out of 180 collected data entries, 176 entries were finally used after data cleaning. About 70% of the final data were used for building the model and the remaining 30% were used for validation. Findings: The proposed model is capable of predicting the pile foundation costs with an accuracy of 97.42%. Originality/value: Although various cost estimation techniques are available, appropriate use and combination of various ML techniques aid in improving the prediction accuracy. The proposed model will be a value addition to cost estimation of pile foundations. © 2023, Emerald Publishing Limited.Item Enhancing infiltration rate predictions with hybrid machine learning and empirical models: addressing challenges in southern India(Springer Science and Business Media Deutschland GmbH, 2025) Ramaswamy, M.V.; Yashas Kumar, H.K.; Reddy, V.J.; Nyamathi, S.J.Despite the success of machine learning (ML) in many disciplines, its application in hydrology, especially in water-scarce regions, faces challenges due to the lack of interpretability and physical consistency. This study addresses these challenges by integrating empirical hydrological models with ML techniques to predict infiltration rates in water-scarce regions of southern India. Using data from 199 observations across 11 sites, including soil characteristics and infiltration measurements, traditional models such as Philip’s, Horton’s, and Kostiakov’s were parameterized and combined with artificial neural networks (ANNs) and the MissForest (MF) algorithm to form hybrid models. The results demonstrate that the hybrid models, particularly those integrating Philip’s model with ANN and multiple predictors, achieved substantial improvements in prediction accuracy, with R2 values ranging from 0.803 to 0.918, root mean-square error (RMSE) from 0.083 to 0.118 cm/min, and Legates’ coefficient of efficiency (LCE) from 0.575 to 0.717 across the target sites. In contrast, empirical models alone at the test sites show lower performance, with R2 ranging from 0.499 to 0.902, RMSE from 0.091 to 0.152 cm/min, and LCE from 0.46 to 0.728, underscoring the limitations of traditional empirical models and the enhancement achieved through ML integration. By leveraging the strengths of empirical models and ML, the hybrid approach improves predictive accuracy and provides a more robust understanding of infiltration dynamics. The hybrid models enable accurate predictions using minimal, readily accessible data, offering a practical solution for water resource management and soil conservation in semi-arid, data-scarce regions. This study demonstrates that blending empirical knowledge with ML algorithms not only improves accuracy but also retains physical interpretability, presenting an innovative solution to hydrological modeling challenges in water-scarce environments. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences 2025.