Faculty Publications
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Publications by NITK Faculty
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Item Artificial intelligence and machine learning in battery materials and their applications(Elsevier, 2024) Acharya, S.; Viswesh, P.; Sridhar, M.K.; Pathak, A.D.; Sharma, H.; Nazir, A.; Kasbe, A.; Sahu, K.K.The fast-depleting fossil fuels and other environmental impacts necessitate rapid development and deployment of efficient, smart, intelligent, and future-ready energy storage solutions. Gone are the days of only trial-and-error-based research and development protocols that take a long time to mature and yield meaningful results, say, in discovering new structures/functional materials (nano to microstructure) for batteries or the development of new battery systems. As the existing computational power is increasing rapidly, coupled with the rapidly falling cost of computation, artificial intelligence (AI) and machine learning (ML) have proved their potential in discovering new battery materials in a short period. This chapter begins with a brief introduction to various AI and ML methods used in the development and deployment of battery material and their applications. Then we focus on the AI and ML methods used in different stages of battery production, from the material selection stage to the manufacturing, state of charge, and state of health prediction, understanding and controlling degrading and aging mechanisms and testing of battery performance, as well as some emerging AI and ML-assisted battery technologies. © 2024 by Elsevier Inc. All rights reserved, including those for text and data mining, AI training, and similar technologies.Item Peer Consonance in Blockchain based Healthcare Application using AI-based Consensus Mechanism(Institute of Electrical and Electronics Engineers Inc., 2020) Kumar, N.; Parangjothi, C.; Guru, S.; Manjappa, M.The term 'Blockchain', commonly referred to as the brain behind the Bitcoin network, works on the simple principle of the presence of a distributed and decentralized ledger in a public or private network. Since blockchain is decentralized, it is the duty of the Consensus Algorithm to substantiate the details in the blockchain. Traditional consensus algorithms such as Proof of Work (PoW) and Proof of Stake (PoS), although widely used, are a matter of concern due to computationally expensive operations and convergence towards a monopolized system respectively. Though optimizations of PoW and PoS algorithms were subsequently introduced, their features precincts. This paper aims to provide a solution by presenting a consensus algorithm based on Artificial Intelligence (AI) technology while maintaining the fairness of the system. A Healthcare based system was set up on top of the blockchain network to generate the dataset about the miners in order to train our neural network. On the whole, it incorporated the advantages of the state of the art consensus models which can increase the efficiency of the healthcare industry while diminishing their demerits. © 2020 IEEE.Item Automated Marine Debris Detection from Sentinel-2 Satellite Imagery(Institute of Electrical and Electronics Engineers Inc., 2024) Priyadarshini, R.; Arya, V.; Sowmya Kamath, S.Marine debris present a severe, escalating threat to oceans and coastal ecosystems, requiring effective monitoring and detection. This work proposes an automated marine debris detection system utilizing satellite imagery data from the MARIDA dataset, sourced from Sentinel-2. Advanced AI techniques are leveraged to analyze high-resolution satellite imagery, and the models are trained to facilitate the identification/tracking of marine debris across various water bodies. Experiments reveal that the machine learning models form a robust baseline, while the UNet model achieves improved precision. The proposed Attention-activated UNet model achieved the best performance, particularly in challenging conditions. © 2024 IEEE.Item Advances in Composite Power System Reliability Assessment: Trends and Machine Learning Role(Institute of Electrical and Electronics Engineers Inc., 2025) Yarramsetty, C.; Moger, T.; Jena, D.; Rao, V.S.This paper provides a detailed review of reliability assessment methods for composite power systems, focusing on integrating renewable energy and advanced computational approaches. The study classifies existing research into three main areas: investigation studies, planning and optimization studies, and efficient evaluation studies. Findings indicate that machine learning techniques are increasingly important in improving accuracy and computational performance in reliability analysis. A comparative examination of conventional and Machine Learning (ML)-based methods demonstrates that deep learning models, such as Convolutional Neural Networks, offer substantial reductions in computational time while maintaining reliability assessment precision. The review also identifies key research gaps, such as the need for realistic test systems and enhanced hybrid ML strategies. Additionally, recommendations are proposed to address these challenges and strengthen the effectiveness of future reliability evaluation methodologies. The insights presented in this study aim to support researchers and industry professionals in developing more efficient and scalable reliability assessment models for evolving power systems. © 2013 IEEE.Item Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl Nitrogen from wastewater(Springer Verlag, 2017) Manu, D.S.; Thalla, A.K.The current work demonstrates the support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) modeling to assess the removal efficiency of Kjeldahl Nitrogen of a full-scale aerobic biological wastewater treatment plant. The influent variables such as pH, chemical oxygen demand, total solids (TS), free ammonia, ammonia nitrogen and Kjeldahl Nitrogen are used as input variables during modeling. Model development focused on postulating an adaptive, functional, real-time and alternative approach for modeling the removal efficiency of Kjeldahl Nitrogen. The input variables used for modeling were daily time series data recorded at wastewater treatment plant (WWTP) located in Mangalore during the period June 2014–September 2014. The performance of ANFIS model developed using Gbell and trapezoidal membership functions (MFs) and SVM are assessed using different statistical indices like root mean square error, correlation coefficients (CC) and Nash Sutcliff error (NSE). The errors related to the prediction of effluent Kjeldahl Nitrogen concentration by the SVM modeling appeared to be reasonable when compared to that of ANFIS models with Gbell and trapezoidal MF. From the performance evaluation of the developed SVM model, it is observed that the approach is capable to define the inter-relationship between various wastewater quality variables and thus SVM can be potentially applied for evaluating the efficiency of aerobic biological processes in WWTP. © 2017, The Author(s).Item Fully automatic segmentation of phalanges from hand radiographs for bone age assessment(Taylor and Francis Ltd., 2019) Simu, S.; Lal, S.; Fadte, K.; Harlapur, A.Segmentation of bones from hand radiograph is an important step in automated bone age assessment (ABAA) system. Main challenges in the segmentation of bones are the intensity inhomogeneity caused by the irregular distribution of X-rays and the overlapping pixel intensities between the bone and soft tissue. Hence, there is a need to develop a robust segmentation technique to tackle the problems associated with the hand radiographs. This paper proposes a fully automatic technique for segmentation of phalanges from left-hand radiograph for bone age assessment. The proposed technique is divided into five stages which are pre-processing, extraction of Phalangeal region of interest, edge preservation, segmentation of phalanges and post-processing. Quantitative and qualitative results of proposed segmentation technique are evaluated and compared with other state-of-the-art segmentation methods. Qualitative results of proposed segmentation technique are also validated by different medical experts. The segmentation accuracy achieved by proposed segmentation technique is 94%. The proposed technique can be used for development of fully ABAA of a person for better accuracy. © 2017, © 2017 Informa UK Limited, trading as Taylor & Francis Group.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 Artificial intelligence approaches for spatial modeling of streambed hydraulic conductivity(Springer International Publishing, 2019) Naganna, S.R.; Deka, P.C.Saturated hydraulic conductivity (Ks) describes the water movement through saturated porous media. The hydraulic conductivity of streambed varies spatially owing to the variations in sediment distribution profiles all along the course of the stream. The artificial intelligence (AI) based spatial modeling schemes were instituted and tested to predict the spatial patterns of streambed hydraulic conductivity. The geographical coordinates (i.e., latitude and longitude) of the sampled locations from where the in situ hydraulic conductivity measurements were determined were used as model inputs to predict streambed Ks over spatial scale using artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) paradigms. The statistical measures computed by using the actual versus predicted streambed Ks values of individual models were comparatively evaluated. The AI-based spatial models provided superior spatial Ks prediction efficiencies with respect to both the strategies/schemes considered. The model efficiencies of spatial modeling scheme 1 (i.e., Strategy 1) were better compared to Strategy 2 due to the incorporation of more number of sampling points for model training. For instance, the SVM model with NSE = 0.941 (Strategy 1) and NSE = 0.895 (Strategy 2) were the best among all the models for 2016 data. Based on the scatter plots and Taylor diagrams plotted, the SVM model predictions were found to be much efficient even though, the ANFIS predictions were less biased. Although ANN and ANFIS models provided a satisfactory level of predictions, the SVM model provided virtuous streambed Ks patterns owing to its inherent capability to adapt to input data that are non-monotone and nonlinearly separable. The tuning of SVM parameters via 3D grid search was responsible for higher efficiencies of SVM models. © 2019, Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences.Item Groundwater level modeling using Augmented Artificial Ecosystem Optimization(Elsevier B.V., 2023) Nguyen, N.; Deb Barma, S.D.; van Lam, T.; Kisi, O.; Mahesha, A.Nature-inspired optimization is an active area of research in the artificial intelligence (AI) field and has recently been adopted in hydrology for the calibration (training) of both process-based and statistical models. This study proposes an improved AI model, Augmented Artificial Ecosystem Optimization-based Multi-Layer Perceptron (AAEO-MLP), to build a monthly groundwater level (GWL) forecasting model. AAEO-MLP model is built on the novel Augmented version of Artificial Ecosystem Optimization and traditional MLP network. In AAEO, Levy-flight trajectory and Gaussian random are utilized in exploration and exploitation to improve the optimizing ability. The AAEO-MLP model is tested on two time-series (1989–2012) datasets collected at two wells in India. Various explanatory variables such as monthly cumulative precipitation, mean temperature, tidal height, and previous measurements of GWL were considered for predicting 1-month ahead of GWL. The performance of AAEO-MLP was benchmarked against 17 different models (original AEO, 3 different variants of AEO, and 13 well-known models) in terms of forecasting accuracy based on six metrics (e.g., mean absolute error, root mean square error, Kling–Gupta efficiency, normalized Nash–Sutcliffe efficiency, Pearson's correlation index, a20 index). Furthermore, convergence analysis and model stability are employed to indicate the effectiveness of AAEO-MLP. The compared results express that the AAEO-MLP is superior to other models in terms of prediction accuracy, convergence, and stability. Overall, the results depict that the AAEO is a promising approach for optimizing machine learning models (e.g., MLP) and should be explored for other hydrological forecasting applications (e.g., streamflow, rainfall) to further assess its strengths over existing methods. © 2022 Elsevier B.V.Item Downscaled XCO2 Estimation Using Data Fusion and AI-Based Spatio-Temporal Models(Institute of Electrical and Electronics Engineers Inc., 2024) Pais, S.M.; Bhattacharjee, S.; Anand Kumar, M.; Chen, J.One of the well-known greenhouse gases (GHGs) produced by anthropogenic human activity is carbon dioxide (CO2). Understanding the carbon cycle and how negatively it affects the ecosystem requires analysis of the rise in CO2 concentration. This work aims to map CO2 concentration for the entire surface, making it useful for regional carbon cycle analysis. Here, column-averaged CO2 dry mole fraction, called XCO2, measured by the orbiting carbon observatory-2 (OCO-2) satellite, is used. Because of spectral interference by the clouds and aerosols, there are many missing footprints in the Level-2 swath of OCO-2, making it disruptive to understand any assessment related to the carbon cycle. The objective of this work is to predict 1 km2 XCO2 using data resampling and machine learning models. This work achieves a minimum mean absolute error (MAE) and root mean square error (RMSE) of 0.3990 and 0.8090 ppm, using the monthly models. © 2004-2012 IEEE.