Faculty Publications
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Item Machine Learning Based Data Quality Model for COVID-19 Related Big Data(Springer Science and Business Media Deutschland GmbH, 2022) Kumar, P.V.; Chandrashekar, A.; Chandrasekaran, K.Big Data is being used in various aspects of technology. The quality of the data being used is essential and needs to be accurate, reliable, and free of defects. The difficulty in improving the quality of big data can be overcome by leveraging computing resources and advanced techniques. In this paper, we propose a solution that utilizes a machine learning (ML) model combined with a data quality model to improve the quality of data. An auto encoder neural network that detects the anomalies in the data is used as the Machine Learning model. This is followed by using the data quality model to ensure the data meets appropriate data quality characteristics. The results obtained from our solution show that the quality of data can be improved efficiently and effortlessly which in turn aids researchers to achieve better results. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Comparative Analysis of Modern Mobile Operating Systems(Institute of Electrical and Electronics Engineers Inc., 2021) Chandrashekar, A.; Kumar, P.V.; Chandavarkar, B.R.The importance of smartphones has grown exponentially since their emergence. Smartphones have fundamentally modified the way people live by allowing them to easily access information and communicate with one another. With this meteoric rise, the operating systems run by these mobile devices have also come a long way in terms of their functionalities and their features. The operating system plays a vital and essential role in the use of these devices and cannot be overlooked. The operating system acts as the foundation of the device. The quality of the operating system directly impacts the quality of the device and also determines the usability of the device. A wide range of mobile operating systems, each with its own set of characteristics and features, currently exist in the market. This paper looks into some of the popular operating systems used in mobile devices and aims to compare and evaluate their different characteristics like architecture, security, and other attributes. This paper also analyzes a few of the advantages and disadvantages of these operating systems. © 2021 IEEE.Item fastText-Based Siamese Network for Hindi Semantic Textual Similarity(Springer Science and Business Media Deutschland GmbH, 2025) Chandrashekar, A.; Rushad, M.; Nambiar, A.; Rashmi, V.; Koolagudi, S.G.Semantic textual similarity is a measurement of the degree of similarity or equivalence between two sentences semantically. Semantic sentence pairs have the ability to substitute text from each other and retain their meaning. Various rule-based and machine learning models have gained quick prominence in the field, especially in a language like English, where there is an abundance of lexical tools and resources. However, other languages like Hindi have not seen much improvement in state-of-the-art methods and models to evaluate semantic similarity of text data. This paper proposes a fastText-based Siamese neural network architecture to evaluate the semantic equivalency between a Hindi sentence pair. The pair is scored on a scale of 0–5, where 0 indicates least similar and 5 indicates most similar. The corpus contains a combination of two datasets containing manually scored sentence pairs. The performance parameters used to evaluate this approach are model accuracy and model loss over a training period of multiple epochs. The proposed architecture incorporates a fastText-based embedding layer and a bi-directional Long Short Term Memory layer to achieve a similarity score. The proposed architecture can extract semantic and various global features of the text to determine a similarity score. This model achieves an accuracy of 85.5% on a compiled Hindi-Hindi sentence pair dataset, which is a considerable improvement over existing rule and supervise-based systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.Item Flexural fatigue analysis of steel fibre reinforced concrete(2012) Girish, M.G.; Chandrashekar, A.; Ravi Shankar, A.U.This paper presents the experimental investigation carried on Steel Fiber Reinforced Concrete (SFRC) subjected to repeated loading. The SFRC beam specimen of size 500mm × 100mm × 100mm containing mixed steel fibers of size 50mm × 2mm × 0.6mm and 0.5mm?× 30mm in different proportions were tested under two point flexural fatigue load at a frequency of 2Hz at various stress levels. The readings obtained from the flexural fatigue test were used to plot S-N diagram and also to perform statistical analysis using two parameter Weibull distributions. © 2012 CAFET-INNOVA TECHNICAL SOCIETY.Item A Study on Elastic Deformation Behavior of Steel Fiber-Reinforced Concrete for Pavements(Springer, 2019) Chandrashekar, A.; Palankar, N.; Durga Prashanth, L.; Mithun, B.M.; Ravi Shankar, A.U.The present study discusses the experimental investigation of steel fiber-reinforced concrete slabs on ground under wheel load with the objective of understanding the stress behavior when subjected to central and edge wheel loading. The steel fiber-reinforced fly ash concrete slabs of 900 mm × 900 mm, 150 mm thickness were investigated in this study. Strain gauges and data acquisition system were used to measure the strains at the center and the edge of the slab under the action of the load. The load versus strain relationship under central and edge loading for reference concrete and steel fiber fly ash concrete showed a linear variation even up to the pressure of 2.5 MPa, which is much beyond the conventional tyre inflation pressure of 0.8 MPa. The load versus strain graphs clearly signify the higher modulus of elasticity of fly ash steel fiber-reinforced concrete. The stresses were calculated using IITRIGID software and ANSYS software and were found matching significantly. The value of modulus of elasticity of fly ash steel fiber-reinforced concrete (FS) using ANSYS model for experimental values of load and strains measured was approximated to 34,000 N/mm2 and was found to closely match with the experimentally obtained modulus of elasticity. No significant effect of Poisson’s ratio of concrete on load–strain characteristics was observed within the range 0.15–0.2 of concrete. © 2019, The Institution of Engineers (India).Item Green covalent surface functionalization of carbon nanofillers and hybridization to improve the thermal and electrical properties of RTV SR nanocomposites(Elsevier Ltd, 2025) Chandrashekar, A.; Hegde, M.; Siya; Karthik Reddy, B.; Jineesh, J.A.; Ravichandran, V.; Eswaraiah, E.; Prabhu, T.N.In this work, graphene (GP) and multiwalled carbon nanotubes (MWCNT) are covalently surface functionalized via a green method using clove extract. The clove–modified carbon hybrid silicone rubber (SR) nanocomposites are fabricated by incorporating clove –modified GP (CGP) and MWCNT (CMWCNT) in various weight ratios with a total filler loading of 10 wt%. Our study investigated the effect of green covalent surface modification and the use of hybrid filler on the thermal and electrical properties of the silicone rubber. The nanocomposite with 9:1 hybrid ratio showed the highest thermal conductivity of about 0.406 W m?1 K?1, 103 % enhancement and thermal effusivity of about 766.2 Ws1/2 m?2 K?1, 29.64 % enhancement with respect to pure SR. Thermal management performance was evaluated by applying thermal compounds as thermal interface material on a 1 W light emitting diode (LED) bulb for testing. It was found that during heating, the hybrid composite with 9:1 ratio showed 2.3 °C reduction in the surface temperature of the LED bulb (under ON condition) and reduced the surface temperature by 1.8 ? within 20 s and reached almost room temperature in 100 s (under OFF condition). In addition, nanocomposite with 9:1 hybrid ratio showed excellent thermal stability, enhanced electrical resistivity which presents a promising strategy for designing thermally conductive polymer nanocomposites based thermal interface materials in managing excess heat for thermal management applications. © 2025Item Enhancement of thermal conductivity in silicone rubber nanocomposites via low loading of polydopamine-coated copper nanowires(Elsevier Ltd, 2025) Hegde, M.; Chandrashekar, A.; Reddy B, K.; Jineesh, J.A.; Ajeya, K.P.; Prabhu, T.N.In recent years, thermally conductive polymer nanocomposites have garnered significant interest due to their wide application in the electronic industry. In the present work, we report thermally conductive silicone rubber-based nanocomposites at lower filler loading of polydopamine-coated copper nanowires (PDA@CuNW). First, copper nanowires (CuNW) are synthesized by the liquid phase reduction method and modified with polydopamine (PDA) by in-situ polymerization. The synthesized CuNW and PDA@CuNW are incorporated into Silicone rubber (SR) varying from 1 to 5 wt% via solution casting. The incorporation of 5 wt% PDA@CuNW resulted in a 62 % improvement in the thermal conductivity of SR. In addition, the nanocomposite showed the highest thermal effusivity of 735 Ws1/2m?2 K?1 even at 5 wt% loading. These results can be attributed to the better adhesion of PDA to the SR matrix confirmed by Field Emission-Scanning Electron Microscopy (FE-SEM). Thermogravimetric analysis showed that the modification of copper nanowires improved the thermal stability of SR. The electrical resistivity of SR increased with the addition of PDA@CuNW. The tensile stress-strain studies reveal that the strength of the SR/PDA@CuNW was improved compared to neat SR and SR/CuNW composites. Moreover, the elongation at break reached up to 972 % which is a 395 % improvement with respect to plain SR. In this work, simultaneous improvement in thermal conductivity and electrical resistivity is achieved while preserving the mechanical properties of the SR nanocomposites. Flexible nanocomposites with improved thermal and electrical properties and minimal filler loading have great significance in high-performance thermal management materials. © 2025 Elsevier LtdItem Synergistic enhancement in thermal conductivity of RTV silicone rubber via non-covalently surface-modified graphene and MWCNT hybrid fillers(Springer, 2025) Chandrashekar, A.; Hegde, M.; Siya Shetty; Reddy, B.K.; Jineesh, J.A.; Varrla, E.; Prabhu, T.N.Effective thermal management is critical for advanced electronic devices, yet conventional polymer-based thermal interface materials (TIMs) often exhibit low thermal conductivity, poor filler dispersion, and high interfacial resistance. This study addresses these limitations by enhancing filler–matrix interactions and exploiting synergistic effects between dual-dimensional carbon nanofillers. Graphene (GPs) and multiwalled carbon nanotubes (MWCNTs) were non-covalently surface modified using phenyl glycidyl ether (PGE) via ultrasonication in THF, improving dispersion and compatibility with room temperature vulcanizing silicone rubber (RTV SR). The surface-functionalized fillers (PGE@GP, PGE@MWCNT) were characterized using FTIR, Raman spectroscopy, FESEM, and TGA to confirm successful modification. Composite films were fabricated by incorporating PGE-modified fillers into RTV SR at three different hybrid ratios (PGE@GP:PGE@MWCNT = 9:1, 8:2, and 7:3) with a total filler content of 10 wt%. The composite with a 9:1 ratio achieved the highest thermal conductivity of 0.459 ± 0.001 Wm?1 K?1, representing a 129.5% enhancement over pure RTV SR. The observed 48.06% synergistic improvement highlights the effectiveness of combining dual-dimensional fillers. Additionally, the composite retained electrical insulation, a critical property for TIM applications. Application tests using a 1 W LED bulb demonstrated the composite’s ability to dissipate heat efficiently, confirming its potential as a high performance, electrically insulating thermal interface material for modern electronic systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.