Faculty Publications
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Publications by NITK Faculty
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Item Enhanced energy output from a PV system under partial shaded conditions through artificial bee colony(Institute of Electrical and Electronics Engineers Inc., 2015) Sundareswaran, K.; Peddapati, P.; Srinivasa Rao Nayak, P.S.R.; Simon, S.P.; S, S.For the maximum utilization of solar energy, photovoltaic (PV) power generation systems are operated at the maximum power point (MPP) under varying atmospheric conditions, and MPP tracking (MPPT) is generally achieved using several conventional methods. However, when partial shading occurs in a PV system, the resultant powervoltage (PV) curve exhibits multiple peaks and traditional methods that need not guarantee convergence to true MPP always. This paper proposes an artificial bee colony (ABC) algorithm for global MPP (GMPP) tracking under conditions of in-homogenous insolation. The formulation of the problem, application of the ABC algorithm, and the results are analyzed in this paper. The numerical simulations carried out on two different PV configurations under different shading patterns strongly suggest that the proposed method is far superior to existing MPPT alternatives. Experimental results are also provided to validate the new dispensation. © 2010-2012 IEEE.Item Novel application of maghemite nanoparticles coated bacteria for the removal of cadmium from aqueous solution(Academic Press, 2020) Devatha, C.P.; S, S.Heavy metals are classified as persistent pollutants owing to their nature of bioaccumulation and affect human life and environment, even in minor concentrations. Divalent Cadmium (Cd2+) is one of the heavy metal pollutants that are highly toxic. The present study investigates the novel application of maghemite nanoparticles coated Bacillus subtilis for the removal of Cd2+ ions from its aqueous solution by batch adsorption studies. Surface characterization of the biosorbent done by Scanning Electron Microscope (SEM) and the presence of maghemite nanoparticle coat was confirmed. Parameters like pH, initial metal ion concentration, contact time, and temperature that affect the biosorption of cadmium ions are analyzed, and the equilibrium adsorption capacity expressed as a function of each of the parameters. The mechanism of biosorption was studied by plotting adsorption isotherms, and it follows pseudo-second-order kinetics. Thermodynamic studies showed the process to be spontaneous and endothermic. At optimum conditions of pH 4, 30 °C, 120 rpm, maximum removal percentage of 83.5%, which accounts for an equilibrium adsorption capacity of 32.6 mg/g of biosorbent. There was a recovery of 76.4% of the biosorbent after adsorption studies. Based on the adsorptive capacity and good recovery of the biosorbent, maghemite coated Bacillus subtilis proves to be an efficient adsorbent for the removal of Cd2+ ions from its aqueous solution. © 2019 Elsevier LtdItem Transfer Learning-Based Fault Diagnosis of Internal Combustion (IC) Engine Gearbox Using Radar Plots(John Wiley and Sons Ltd, 2024) S, S.; Srivatsan, B.; Sugumaran, V.; Ravikumar, K.N.; Kumar, H.; Mahamuni, V.S.Due to constant loads, gear wear, and harsh working conditions, gearboxes are subject to fault occurrences. Faults in the gearbox can cause damage to the engine components, create unnecessary noise, degrade efficiency, and impact power transfer. Hence, the detection of faults at an early stage is highly necessary. In this work, an effort was made to use transfer learning to identify gear failures under five gear conditions—healthy condition, 25% defect, 50% defect, 75% defect, and 100% defect—and three load conditions—no load, T1 = 9.6, and T2 = 13.3 Nm. Vibration signals were collected for various gear and load conditions using an accelerometer mounted on the casing of the gearbox. The load was applied using an eddy current dynamometer on the output shaft of the engine. The obtained vibration signals were processed and stored as vibration radar plots. Residual network (ResNet)-50, GoogLenet, Visual Geometry Group 16 (VGG-16), and AlexNet were the network models used for transfer learning in this study. Hyperparameters, including learning rate, optimizer, train-test split ratio, batch size, and epochs, were varied in order to achieve the highest classification accuracy for each pretrained network. From the results obtained, VGG-16 pretrained network outperformed all other networks with a classification accuracy of 100%. © © 2024 S. Naveen Venkatesh et al.Item RMDNet-Deep Learning Paradigms for Effective Malware Detection and Classification(Institute of Electrical and Electronics Engineers Inc., 2024) S, S.; Lal, S.; Pratap Singh, M.; Raghavendra, B.S.Malware analysis and detection are still essential for maintaining the security of networks and computer systems, even as the threat landscape shifts. Traditional approaches are insufficient to keep pace with the rapidly evolving nature of malware. Artificial Intelligence (AI) assumes a significant role in propelling its design to unprecedented levels. Various Machine Learning (ML) based malware detection systems have been developed to combat the ever-changing characteristics of malware. Consequently, there is a growing interest in exploring advanced techniques that leverage the power of Deep Learning (DL) to effectively analyze and detect malicious software. DL models demonstrate enhanced capabilities for analyzing extensive sequences of system calls. This paper proposes a Robust Malware Detection Network (RMDNet) for effective malware detection and classification. The proposed RMDNet model branches the input and performs depth-wise convolution and concatenation operations. The experimental results of the proposed RMDNet and existing DL models are evaluated on 48240 malware and binary visualization image dataset with RGB format. Also on the multi-class malimg and dumpware-10 datasets with grayscale format. The experimental results on each of these datasets demonstrate that the proposed RMDNet model can effectively and accurately categorize malware, outperforming the most recent benchmark DL algorithms. © 2013 IEEE.Item Cerium-Modulated Zinc Oxide for enhanced Photoelectrochemical Non-Enzymatic biosensing of Cholesterol: An experimental and First Principle Analysis(Elsevier B.V., 2024) Rao, L.; Rodney, J.D.; Joy, A.; Shivangi Nileshbhai, C.; James, A.; S, S.; Joyline Mascarenhas, F.; Udayashankar, N.K.; Anjukandi, P.; Chul Kim, B.; Badekai Ramachandra, B.R.Herein, we synthesized CexZn1-xO (x = 0.00, 0.01, 0.02, and 0.03) using the wet chemical method. The investigation explores photoelectrochemical (PEC) biosensors for enzyme-free detection of cholesterol, employing Ce0.03Zn0.97O (CZO3)/Nickel Foam (NF) as the active material. The investigation revealed notable enhancements in sensitivity for cholesterol detection, with a recorded activity of 2.812 mA.mM?1.cm?2, marking a twofold increase in comparison to dark mode (1.37 mA.mM?1.cm?2). The Limit of Detection (LOD) was determined to be 17 µM (light) and 28 µM (dark), while the Limit of Quantification (LOQ) was measured at 54 µM (light) and 98 µM (dark) in 0.1 M KOH solution. These findings demonstrate a linear detection range spanning from 80 µM to 2 mM. Ab-initio calculations based on Density Functional Theory (DFT) were carried out on 101 surfaces of both pristine ZnO and CZO3 to understand how the doping affected the pristine ZnO band gap. The findings indicate that CZO3 exhibits superior activity compared to pristine ZnO, underscoring its enhanced performance and potential for sensing application. The CZO3/NF photoelectrochemical (PEC) biosensor displayed notable cyclic stability, retaining 97 % of its performance over a 60-day period. This underscores its potential for reliable and enduring operation in biosensing applications. Additionally, CZO3/NF exhibited robust sensing capabilities when utilized with human serum samples, showcasing consistent performance in both dark and illuminated conditions. © 2024 Elsevier B.V.Item An empirical examination of user’s adoption of AR in e-commerce apps in a developing country: evidence from India(Emerald Publishing, 2025) Anand, K.; S, S.Purpose The e-commerce industry is witnessing rapid growth by offering convenience in shopping. However, it fails to provide a virtual experience of the products, creating excessive product returns and posing challenges to the companies, customers and environment. The use of augmented reality (AR) bridges the gap between the customers and products. The purpose of this study is to explore the customer perspective towards adopting AR in online shopping, which can reduce excessive product returns, creating sustainable business practices. Design/methodology/approach Online survey was used in the data collection process. A total of 1,029 valid responses collected via judgement sampling were analysed using covariance-based structural equation modelling with AMOS software. Findings Trust is significantly influenced by performance expectancy, effort expectancy, social influence and facilitating conditions. Likewise, performance expectancy, hedonic motivation and trust significantly affect behavioural intention. Meanwhile, effort expectancy, social influence, facilitating conditions on behavioural intention and hedonic motivation on trust were found to have an insignificant influence. Practical implications This study investigates users’ behavioural intention towards AR in online shopping apps using the Unified Theory of Acceptance and Use of Technology (UTAUT2) with trust in the Indian context, providing valuable insights into implementing AR technology and enhancing the realism of virtual product experiences. Originality/value This study aims to investigate users’ behavioural intention by providing a research model that extends the UTAUT2 model, including trust. The objective is to analyse the factors influencing the adoption of a novel technology in a developing country, specifically in the Indian context. © 2025 Emerald Publishing LimitedItem Advancements of virtual reality in tourism and hospitality research. A hybrid review using the TCCM framework(Routledge, 2025) Talawar, A.; S, S.; Alathur, S.This paper aims to explore the advancements in VR research within tourism and hospitality industry using a hybrid review approach combining bibliometric analysis and TCCM framework. The study uncovers the past, present, and emerging research themes in the field, with most studies grounded in theoretical foundations (T); it presents eleven distinct contexts of VR applications (C); and indicates VR experiences mostly enhance behavioral intentions to visit (C); quantitative approaches, such as surveys and experiments are preferred over qualitative methods (M). Finally, this study proposes a conceptual model and nomological network, along with directions for future research through the TCCM perspective. © 2024 Asia Pacific Tourism Association.Item Pd/C-decorated SnO2 for advanced non-enzymatic cholesterol biosensing: analytical application in clinical blood specimens(Elsevier Inc., 2025) Rao, L.; Rodney, J.D.; S, S.; Mascarenhas, F.J.; Nayak, M.P.; Kim, B.C.; Badekai Ramachandra, B.R.Cholesterol, a critical biomolecule, plays a vital role in physiological functions; however, elevated levels are associated with chronic conditions such as cardiovascular diseases, which remain a leading cause of mortality globally. To address this challenge, this study presents the synthesis of SnO2-Pd/C nanocomposite through a two-step process as a promising material for non-enzymatic cholesterol biosensing. Initially, SnO2 was synthesized via a hydrothermal method and subsequently decorated with Pd/C. The resulting SnO2-Pd/C nanocomposite was integrated with nickel foam (NF) as the active material for biosensor development. The biosensor demonstrated a remarkable sensitivity of 1560 µA mM?1 cm?2 for cholesterol detection, which is approximately three times higher than that of SnO2-NF (546 µA mM?1 cm?2). Key performance metrics included a Limit of Detection (LOD) of 28 µM and a Limit of Quantification (LOQ) of 34 µM in 0.1 M KOH solution, with a linear detection range extending from 200 µM to 2 mM. The SnO2-Pd/C-NF biosensor exhibited outstanding cyclic stability, retaining 97 % of its performance over 30 days, underscoring its potential for reliable and long-term applications. Furthermore, the sensor demonstrated robust and consistent sensing performance with human serum samples under standard conditions, highlighting its practical applicability in clinical diagnostics. © 2025 Elsevier B.V.Item Recycling waste plastics and biowaste into high-performance NiCo-MOF/activated carbon electrocatalyst for overall water splitting(Elsevier Ltd, 2025) Nayak, M.P.; Rao, L.; Rodney, J.D.; S, S.; Rohit, A.G.; Badekai Ramachandra, B.R.Environmental and energy crises are the most significant global challenges. Developing non-precious and environmentally sustainable electrocatalysts remains critical for advancing renewable hydrogen production. This study presents a novel hybrid electrocatalyst comprising a NiCo-BDC Metal-Organic Framework (NiCo-MOF), where the BDC (Benzene 1,4-di carboxylic acid) ligand was obtained by recycling waste poly(ethylene terephthalate) (PET) bottles, integrated with activated carbon (AC) derived from dried drumstick (Moringa olifera) biowaste, via a one-pot hydrothermal method. The research emphasizes optimizing the AC content within the MOF matrix to enhance catalytic performance. The synergistic interaction between NiCo-MOF and AC significantly reduces the overpotentials required for the Hydrogen Evolution Reaction (HER) and Oxygen Evolution Reaction (OER) in an alkaline medium. Notably, the optimized composite, NiCo-MOF@40AC, exhibited enhanced crystallinity, BET surface area, and electrocatalytic activity. At a current density of 100 mA cm?2, NiCo-MOF@40AC achieved overpotentials as low as 217 mV for HER with a Tafel slope of 105.6 mV dec?1 and 315 mV for OER with a Tafel slope of 42.2 mV dec?1. Furthermore, this material demonstrated robust stability over a 24 h chrono potentiometric test, maintaining performance at an elevated current density of 200 mA cm?2. In a two-electrode system, NiCo-MOF@40AC needed only 1.58 V to sustain a current density of 10 mA cm?2, exhibiting stability over 48 h and 24 h at a current density of 10 mA cm?2 and 400 mA cm?2, respectively. An average faradaic efficiency was found to be 93.48 % for HER and 91.91 % for OER. These findings highlight the potential of NiCo-MOF@40AC as an efficient electrocatalyst, characterized by a high surface area, rapid electron transfer, favorable structural properties, and enhanced reaction kinetics. © 2025 Hydrogen Energy Publications LLC