Functionalized Nanomaterials in Nanobiomedicines and Diagnostic Devices
Bioimaging; Diagnostic Devices; Drug Delivery; Nanobiomedicine; Nanomaterials; Surface functionalization
The development of nanomaterials for biomedical applications over the last 20 years has shown great potential for altering the way that all facets of illness management are thought to be approached. Since nanomaterials may be customized to incorporate features for early diagnosis, medication delivery, therapy, treatment, and patient response monitoring, they are particularly appealing as a system. The realm of functionalized nanomaterials is characterized by its diversity and rapid evolution, with ongoing research endeavors focused on discovering novel functionalities and applications for nanoscale materials. Functionalized nanomaterials play a pivotal role in nanobiomedicines and diagnostic devices, contributing significantly to advancements in these fields. The use of functionalized nanomaterials varies from cancer therapy, drug delivery, cosmetics, and medical use to various other entities. Therefore, this chapter reviews the fundamentals of nanomaterials fictionalization, surface fictionalization, and different nanomaterials that are contributing to drug delivery and diagnostic devices. Springer Nature Singapore Pte Ltd. 2025.
Molahalli, Vandana; Unnimaya, S.; Chattham, Nattaporn; Hegde, Gurumurthy
Handbook of Material Engineering in Nanobiomedicine and Diagnostics;pp.245-259
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-981-97-7445-6_11 https://www.scopus.com/pages/publications/105007965672?origin=resultslist
Molahalli V., Department of Physics, B.M.S. College of Engineering, Bengaluru, India; Unnimaya S., Centre for Advanced Research and Development (CARD), Christ University, Bengaluru, India; Chattham N., Department of Physics, Faculty of Science, Kasetsart University, Bangkok, Thailand; Hegde G., Centre for Advanced Research and Development (CARD), Christ University, Bengaluru, India, Department of Chemistry, Christ University, Bengaluru, India
An Efficient Multi-Modal Classification Approach for Disaster-related Tweets
Convolution Neural Network; Deep Learning; Disaster Tweet; Gated Recurrent Unit; Late Fusion; VGG-16
Owing to the unanticipated and thereby treacherous nature of disasters, it is essential to gather necessary information and data regarding the same on an urgent basis; this helps to get a detailed overview of the situation and helps humanitarian organizations prioritize their tasks. In this paper, "An Efficient Multi-Modal Classification Approach for Disaster-related Tweets,"the proposed framework based on Deep Learning to classify disaster-related tweets by analyzing text and image contents. The approach is based on Gated Recurrent Unit (GRU) and GloVe Embedding for text classification and VGG-16 network for image classification. Finally, a combined model is proposed using both text and image modules by the Late Fusion Technique. This portrays that the proposed multi-modal system performs significantly well in classifying disaster-related content. 2022 IEEE.
Mondal A.; Kesan A.; Rodrigues A.; George J.
IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICDCECE53908.2022.9792951 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133428851&doi=10.1109%2fICDCECE53908.2022.9792951&partnerID=40&md5=688a26df6821cad4e8d36801c58adb26
Mondal A., Deemed to Be University, Department of Data Science Christ, India; Kesan A., Deemed to Be University, Department of Data Science Christ, India; Rodrigues A., Deemed to Be University, Department of Data Science Christ, India; George J., Deemed to Be University, Department of Data Science Christ, India
Tracing the outer disk of NGC300: An ultraviolet view
Individual galaxies; spiral galaxies; star formation; XUV disk
We present an ultra-violet (UV) study of the galaxy NGC300 using GALEX far-UV (FUV) and near-UV (NUV) observations. We studied the nature of UV emission in the galaxy and correlated it with optical, HI and mid-infrared (3.6 ?m) wavelengths. Our study identified extended features in the outer disk, with the UV disk extending up to a radius of 12 kpc (> 2 R25). We estimated the FUV and NUV disk scale-length as 3.05 0.27 kpc and 2.66 0.20 kpc respectively. The scale-length in FUV is 2.3 times larger than that at 3.6 ?m, and we also find the disk to gradually become flatter from longer to shorter wavelengths. We performed a statistical source subtraction to eliminate the background contaminants and identified 261 unresolved UV sources between the radii 5.3 kpc and 10 kpc (1 ? 2 R25). The identified UV sources show an age range between 1300 Myr with a peak at 25 Myr and a mass range between 10 3M? to 10 6M?, estimated using Starburst99 models. The north-eastern spiral arm is found to be populated by young low mass sources suggesting that the star formation in this spiral arm is a recent phenomenon. The UV emission beyond the R25 radius has contribution from these low mass sources and is extended up to ? 2 R25 radius. We conclude that NGC300 has an extended UV disk, mainly populated by young low mass sources. The star formation rate is measured to be ?0.46M?/yr which is comparable to its near optical twin M33. 2019, Indian Academy of Sciences.
Journal of Astrophysics and Astronomy, Vol-40, No. 4
Mondal C., Indian Institute of Astrophysics, 2nd Block, Koramangala, 560 034, Bengaluru, India, Pondicherry University, R.V. Nagar, Kalapet, 605 014, India; Subramaniam A., Indian Institute of Astrophysics, 2nd Block, Koramangala, 560 034, Bengaluru, India; George K., Department of Physics, Christ University, Bengaluru, India
Categorization of artwork images based on painters using CNN
Convolutional Neural Network; Deep Learning; Neural Network; Painter Identification; Painting Classification
Artworks and paintings has been an integral part of human civilization since the dawn of the Stone Age. Paintings gives more insight about any subject compared to the scriptures and documents. Archiving of digital form of paintings helps to preserve the artworks of different painters. The anticipated work is aimed for the classification of painters' artworks. The artworks of Foreign & Indian painters are considered for the proposed work. The foreign painters' artworks are obtained from [14]. At present, the Indian painters' artwork dataset is not readily available. The images were downloaded from the specific genuine website [13]. Conventional Neural Network is used for Feature learning and classification. Around 20k images of artworks is used for the experiment and got an average accuracy of 85.05%. Published under licence by IOP Publishing Ltd.
Journal of Physics: Conference Series, Vol-1818, No. 1
Mondal K., Department of Computer Science, Christ (Deemed to Be University), Bengaluru, Karnataka, India
Interpenetrated Robust Metal-Organic Framework with Urea-Functionality-Decked Pores for Selective and Ultrasensitive Detection of Antibiotics and Oxo-anions
Conjoining the benefits of structural diversity and deliberate implantation of task-specific sites inside the porous channels, metal-organic frameworks (MOFs) not only ensure environmental remediation via acute detection of organic as well as inorganic pollutants but also rationalize structure-performance synergies to devise smarter materials with advanced performances. Herein, we report a urea-functionality-grafted Co(II)-framework (UMOF) based on a mixed ligand approach. The 3-fold interpenetrated and [Co2(COO)4N4] building unit-containing structure exhibits high stability and free-carboxamide-site-decorated microporous channels. Assimilation of high-density hydrogen-bond donor groups plus the ?-electron-rich aromatic ligand benefits the UMOF acting as a selective fluoro-sensor for three noxious antibiotics through remarkable quenching, including nitrofurazone (NFT, Ksv: 3.2 104 M-1), nitrofurantoin (NFZ, Ksv: 3.0 104 M-1), and sulfamethazine (SMZ, Ksv: 3.3 104 M-1) with ppb level limits of detection (LODs, NFT: 110.42, NFZ: 97.89, and SMZ: 78.77). The mechanistic insight of luminescence quenching is supported from density functional theory calculations, which endorse the electron-transfer route via portraying variation in the energy levels of the urea group-affixed linker by individual organo-toxins, besides verifying analyte-linker noncovalent interactions. The framework further demonstrates highly discriminative turn-off detection of oxo-anions with extreme low LODs (Cr2O72-: 73.35; CrO42-: 189; and MnO4-: 49.96 ppb). Of note is the reusability of the UMOF toward multicyclic sensing of all the organic and inorganic analytes besides their fast-responsive detection, where variable magnitudes of energy-transfer contributions unequivocally authenticate the turn-off event. 2023 American Chemical Society.
Mondal P.P.; Muthukumar D.; Fathima S.K.P.; Pillai R.S.; Neogi S.
Crystal Growth and Design, Vol-23, No. 11, pp. 8342-8351.
https://doi.org/10.1021/acs.cgd.3c00979 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176112971&doi=10.1021%2facs.cgd.3c00979&partnerID=40&md5=26475c6486cfebcf989e2dc8e805ba3e
Mondal P.P., Inorganic Materials & Catalysis Division, CSIR-Central Salt & Marine Chemicals Research Institute (CSMCRI), Gujarat, Bhavnagar, 364002, India; Muthukumar D., Department of Chemistry, Christ University, Karnataka, Bangalore, 560029, India; Fathima S.K.P., Inorganic Materials & Catalysis Division, CSIR-Central Salt & Marine Chemicals Research Institute (CSMCRI), Gujarat, Bhavnagar, 364002, India; Pillai R.S., Department of Chemistry, Christ University, Karnataka, Bangalore, 560029, India, Analytical and Spectroscopy Division, ASCG/PCM, Indian Space Research Organization, Vikram Sarabhai Space Centre, Kerala, Thiruvananthapuram, 695 022, India; Neogi S., Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India, Inorganic Materials & Catalysis Division, CSIR-Central Salt & Marine Chemicals Research Institute (CSMCRI), Gujarat, Bhavnagar, 364002, India
Spectral and temporal studies of Swift J1658.24242 using AstroSat observations with the JeTCAF model
accretion, accretion discs; black hole physics; ISM: jets and outflows; shock waves; X-rays: binaries; X-rays: individual: Swift J1658.2-4242
We present the X-ray spectral and temporal analysis of the black hole X-ray transient Swift J1658.2-4242 observed by AstroSat. Three epochs of data have been analysed using the JeTCAF model to estimate the mass accretion rates and to understand the geometry of the flow. The best-fitting disc mass accretion rate (? d) varies between 0.90+-000102 and 1.09+-000304 M?Edd in these observations, while the halo mass accretion rate changes from 0.15+-000101 to 0.25+-000102 M?Edd. We estimate the size of the dynamic corona that varies substantially from 64.9+-3319 to 34.5+-1250 rg and a moderately high jet/outflow collimation factor stipulates isotropic outflow. The inferred high disc mass accretion rate and bigger corona size indicate that the source might be in the intermediate to soft spectral state of black hole X-ray binaries. The mass of the black hole estimated from different model combinations is ?14 M?. In addition, we compute the quasi-periodic oscillation (QPO) frequencies from the model-fitted parameters, which match the observed QPOs. We further calculate the binary parameters of the system from the decay profile of the light curve and the spectral parameters. The estimated orbital period of the system is 4.0 0.4 h by assuming the companion as a mid or late K-type star. Our analysis using the JeTCAF model sheds light on the physical origin of the spectrotemporal behaviour of the source, and the observed properties are mainly due to the change in both the mass accretion rates and absorbing column density. 2023 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society.
Monthly Notices of the Royal Astronomical Society, Vol-522, No. 2, pp. 2065-2074.
https://doi.org/10.1093/mnras/stad1058 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161483959&doi=10.1093%2fmnras%2fstad1058&partnerID=40&md5=85227dfa5ff7cb552223a8de3b15d0d6
Mondal S., Indian Institute of Astrophysics, II Block Koramangala, Bengaluru, 560034, India; Jithesh V., Department of Physics and Electronics, CHRIST (Deemed to be University), Hosur Main Road, Bengaluru, 560029, India
Two-dimensional chromium telluride-coated 3D-printed architectures for energy harvesting
Rapid development of industries, urbanization, and technological advancements have increased demand for sustainable and cost-effective alternative energy sources. In this work, a self-powered flexible 3D-printed triboelectric nanogenerator coated with 2D chromium telluride (Cr2Te3) (3D-TENG) is presented as an innovative energy harvesting approach from pressure and temperature. The optimized flexible 3D-printed hexagonal structures with coatings show varying specific yield strength and porosity. The 3D-TENGs achieved a maximum output voltage of ?39 V under periodic impacts of ~0.8 kPa and their performance further increased (?45 V) in the presence of varied temperatures. The outstanding results and flexibility of the 3D-TENG devices highlight their potential in self-powered energy harvesting from external heat, magnetic fields, and body weight. Density functional theory (DFT) calculations further explained the interaction between 2D Cr2Te3 and the polymer surface under external impact. Therefore, we believe that our findings illustrate the potential of integrating 2D materials with 3D-printed architectures to enhance the efficiency and adaptability of flexible, lightweight, low-cost, and eco-friendly TENG devices for industrial applications. 2025 The Royal Society of Chemistry.
Mondal, Ritam; Mathias, Royston; Bastos, Leonardo V.; Gowda, Chinmayee Chowde; Tiwari, Nishant; Singh, Himanshu; Woellner, Cristiano F.; Tiwary, Chandra Sekhar; Kumbhakar, Partha
All Open Access; Hybrid Gold Open Access
https://doi.org/10.1039/d4nr05005c https://www.scopus.com/pages/publications/105003732766?origin=resultslist
Mondal R., Department of Physics and Electronics, Christ University, Bangalore, 560029, India; Mathias R., Department of Physics and Electronics, Christ University, Bangalore, 560029, India; Bastos L.V., Physics Department, Federal University of Paran UFPR, PR, Curitiba, 81531-980, Brazil; Gowda C.C., School of Nano Science and Technology, Indian Institute of Technology, West Bengal 721302, Kharagpur, India; Tiwari N., Department of Metallurgical and Materials Engineering, Indian Institute of Technology, West Bengal, Kharagpur, 721302, India; Singh H., Department of Metallurgical and Materials Engineering, Indian Institute of Technology, West Bengal, Kharagpur, 721302, India; Woellner C.F., Physics Department, Federal University of Paran UFPR, PR, Curitiba, 81531-980, Brazil; Tiwary C.S., Department of Metallurgical and Materials Engineering, Indian Institute of Technology, West Bengal, Kharagpur, 721302, India; Kumbhakar P., Department of Physics and Electronics, Christ University, Bangalore, 560029, India
Exploring cybernetic approaches to sustainable co-working spaces in emerging economies: a sentiment analysis
Circular economy; Co-working spaces; Cybernetic principles; India and UAE; Shared economy; Sustainability
Purpose In quest of achieving long-term sustainability of co-working spaces (CWSs) and drawing on the cybernetic principles, this study aims to develop a resilient business model promoting economic viability, encouraging environmental responsibility and reinforcing its social impact. Furthermore, to address the transformative shift in way people work in emerging economies, this study probed respondents from India and United Arab Emirates (UAE) and finally identified critical challenges and opportunities bringing in maximum customer satisfaction and achieving long-term business profitability. Design/methodology/approach Using a multi-method qualitative triangulation approach (sentiment analysis), the study collected primary data from India and UAE, analysed through the grounded theory approach. Whereas secondary data in form of tweets was tested using text-mining approach using NVivo. The findings from the dual study were corroborated to identify common dimensions, leading to the development of a hypothetical framework. Findings In CWSs business, dynamic organisation culture holds key in fostering future sustainability, and the study has explored its important antecedents like adaptive management, continuous innovation and technological integration. The impact of these antecedents was found to be moderated by two critical dimensions of regulatory challenges and competitive landscape. Furthermore, the study delved into connecting with the principles of circular economy moderating the impact of dynamic organisation culture towards long-term sustainability of CWSs. Practical implications This study applies cybernetic principles alongside shared and circular economy frameworks to assess consumer perceptions of CWSs. The insights generated can guide researchers, entrepreneurs, urban planners and policymakers in designing flexible business models, strengthening community networks and exploring diverse revenue streams to enhance resilience and long-term growth. Originality/value This research provides empirical evidence on the sustainability dynamics of CWSs, offering a balanced perspective on overcoming challenges and leveraging growth opportunities. Additionally, it bridges the concepts of cybernetics, shared economy and circular economy, presenting a novel framework for ensuring the sustainable development of CWS businesses. 2025 Emerald Publishing Limited
https://doi.org/10.1108/F-03-2025-0047 https://www.scopus.com/pages/publications/105022725903?origin=resultslist
Mondal S., School of Business and Management, CHRIST (Deemed to be University), Bengaluru, India; Samaddar K., SIU SIBM Bengaluru, Bangalore, India
Fostering cultural vitality and enhancing sustainable urban tourism through international labelling: anethno-morphological exploration
Cultural tourism; Cultural vitality; Ethno-morphological analysis; International labelling; Sustainable growth; Urban tourism
Purpose Cultural Vitality (CV) nurtures creativity, enhances engagement with the local community and supports the art and culture of the place. In quest of exploring the impact of International Labelling (IL) in enhancing CV and reinforcing Sustainable Development (SD), the study pursues a comprehensive Morphological Analysis (MA) and builds a hypothetical framework bridging culture-driven urban tourism with sustainable growth. Design/methodology/approach An ethno-morphological study based on the Durga Puja Festival (India) was adopted to identify the critical dimensions of culture-driven urban tourism. A series of in-depth interviews and cross-sectional studies were carried out to develop a hypothetical research framework for empirical (quantitative) validation thereon. Findings Critical dimensions such as Cultural Investment, Collaborative Partnership and Embracing Glocal Approach were identified as major constructs towards achieving CV of a destination. IL and Responsible Consumption were found to moderate the effect of antecedents and CV upon sustainable growth. Research limitations/implications The present research limits its scope to the geographical boundary within India, keeping cross-boundary research for future study. This study will aid future researchers and scholars in expanding the domain of culture-driven urban tourism. Practical implications The present study bears significance to the urban policymakers, governing bodies, marketers and tour operators in embracing a culture-driven perspective while undertaking a suitable strategy towards developing CV, promoting urban-tourism attributes and vis-vis ensuring its SD. Originality/value This study makes a novel attempt at adopting an ethno-morphological approach and blending culture-driven tourism with sustainable growth while exploring the impact of IL, all together in a research initiative, making it a single-point reference in urban tourism literature. Emerald Publishing Limited
Journal of Cultural Heritage Management and Sustainable Development;pp.1-18
https://doi.org/10.1108/JCHMSD-05-2024-0093 https://www.scopus.com/pages/publications/105037077217?origin=resultslist
Mondal S., Department of Marketing, Faculty of Commerce and Management, Christ University, Bangalore, India; Samaddar K., SIU SIBM Bengaluru, Bangalore, India
Investigation on Electrode/Electrolyte Interfaces through Impedance Spectroscopy
Cathode; interfaces; solid electrolyte interface (SEI)
In the present paper, impedance measurements of the battery configuration, Anode?lithium borophosphate glass electrolyte?LiCoO2 cathode, has been carried out to throw some light on the electrochemical interfacial behavior between the chosen electrodes and electrolyte. The cathode material, lithium cobalt oxide (LiCoO2) has been prepared by three different techniques and characterized. Sol-gel synthesized LiCoO2 showed uniformly distributed spherical shape particles with an average size of 500 nm and also exhibited better electrochemical performance. Charging and discharging (23 cycles) of the battery indicated an OCV of 2 V. However, the theoretical OCV of 4 V could not be achieved. The poor performance of the battery could be attributed to the electrochemical processes and SEI film formation at the electrode/electrolyte interfaces. Impedance spectroscopy shows that the major contributions to the impedance of the battery are the electrolyte resistance and the electrode/electrolyte interfacial resistance. With each recharging cycle, the value of electrolyte resistance remains almost constant; however, the interface resistance increases, during the passage of current, due to the interfacial passive layer formation. 2020 Taylor & Francis Group, LLC.
Integrated Ferroelectrics, Vol-212, No. 1, pp. 96-103.
Money B.K., Department of Sciences & Humanities, Faculty of Engineering, CHRIST University, Bangalore, India; Saji J., Department of Sciences & Humanities, Faculty of Engineering, CHRIST University, Bangalore, India; Hariharan K., Department of Physics and Centre for Sponsored Research & Consultancy, Rajalakshmi Engineering College, Anna University, Chennai, India
Secure Decentralization: Examining the Role of Blockchain in Network Security
Blockchain; Communication; Cryptographic; Decentralized; Preventing
Blockchain generation has emerged as a novel answer for securing decentralized networks. This technology, which was first created for use in crypto currencies, has received enormous interest in recent years because of its capability for boosting protection in various industries and community protection. The essential precept at the back of block chain technology is the decentralization of statistics garage and control. In a decentralized network, no central authority may control the statistics. Rather, the facts are shipped amongst multiple nodes, making it immune to tampering and single factors of failure. One of the most important advantages of blockchain in community protection is its capacity to offer cozy and transparent communication amongst community customers. Through cryptographic techniques, block chain can affirm the identities of network participants and ensure the authenticity of records trade. This feature is extraordinarily valuable in preventing unauthorized access and facts manipulation. 2024 IEEE.
Monga S.; Gupta P.; Logeshwaran J.; Thamaraimanalan T.
2024 2nd World Conference on Communication and Computing, WCONF 2024
https://doi.org/10.1109/WCONF61366.2024.10692123 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207456667&doi=10.1109%2fWCONF61366.2024.10692123&partnerID=40&md5=199c35b5f68e5fd9fff4ac38e0a1a8bb
Monga S., Chitkara University, Institute of Engineering and Technology, Chitkara University, Punjab, Rajpura, India; Gupta P., Chitkara University, Institute of Engineering and Technology, Chitkara University, Punjab, Rajpura, India; Logeshwaran J., Department of Computer Science, CHRIST (Deemed to be University, Karnataka, Bengaluru, 560029, India; Thamaraimanalan T., Department of Electronics andCommunication Engineering, Sri Eshwar College of Engineering, Tamil Nadu, Coimbatore, 641202, India
Entrepreneurial Attitude and Entrepreneurial Intentions of Female Engineering Students: Mediating Roles of Passion and Creativity
economic development; employment; engineering; entrepreneurship; innovation
Entrepreneurship holds a crucial function in addressing societal and economic issues like joblessness and inequalities between different regions. Acknowledging its significance, government officials and educational institutions exert considerable energy towards nurturing individuals into entrepreneurs. Multiple elements influence a person's path to becoming an entrepreneur. This research seeks to examine how one's entrepreneurial attitude (EA) impacts one's drive to become an entrepreneur, with passion and creativity serving as an intermediary in this connection. The research is explanatory and employs a survey-based approach. The findings convey that entrepreneurial attitude significantly influences the determination of female engineering students to pursue entrepreneurship. The study highlights the mediating roles of passion and creativity in the relationship between entrepreneurial attitude and intentions. While passion positively mediated the relationship, creativity had a negative mediating effect. 2024, Institute of Economic Sciences. All rights reserved.
Journal of Women's Entrepreneurship and Education, Vol-2024, No. 45689, pp. 19-39.
https://doi.org/10.28934/jwee24.12.pp19-39 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192703353&doi=10.28934%2fjwee24.12.pp19-39&partnerID=40&md5=9c6a6a54e257af7c0b711fb4b6b519f0
Monica J., CHRIST (Deemed to be University), School of Commerce, Finance and Accountancy, Bangalore, India; Anuradha P., CHRIST (Deemed to be University), School of Commerce, Finance and Accountancy, Bangalore, India
Nexus between Entrepreneurial Education, Entrepreneurial Mindset, and Entrepreneurial Passion on Entrepreneurial Intentions: Mediating Role of Self-efficacy
Entrepreneurial Education; Entrepreneurial Mindset; Entrepreneurial Passion on Entrepreneurial Intentions; Self-efficacy
This study examines the complex dynamics of factors affecting self-efficacy (SE) and entrepreneurial intentions (EIs) among engineering students in India. It investigates the mediating role of SE in the relationships between entrepreneurial education (EE), entrepreneurial mindset (EM), entrepreneurial passion (EP), and EIs. The research reveals that SE remains stable across various personal characteristics, highlighting it as a robust individual trait less influenced by external factors. Gender significantly impacts EIs, underscoring its pivotal role in shaping entrepreneurial intentions, while other personal characteristics show limited influence. Passion and mindset appear to be consistent across demographics, suggesting they are intrinsic qualities. SE serves as a mediator in the connections between entrepreneurial mindset, passion, and intentions, elucidating its pivotal role in the entrepreneurial process. EE indirectly affects EIs and SE through other factors in the research model. Entrepreneurial passion directly influences both EIs and SE, emphasizing its role as a driving force for entrepreneurship. An entrepreneurial mindset doesn't directly affect intentions but significantly influences SE, indicating its importance in shaping self-efficacy, which in turn influences intentions. The findings can guide the development of educational programs and initiatives designed to promote entrepreneurship among engineering students in India while considering the impact of self-efficacy and gender-related factors. 2024, Iquz Galaxy Publisher. All rights reserved.
International Research Journal of Multidisciplinary Scope, Vol-5, No. 4, pp. 1231-1243.
https://doi.org/10.47857/irjms.2024.v05i04.01759 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210448953&doi=10.47857%2firjms.2024.v05i04.01759&partnerID=40&md5=0dd65fb260afcc22fd7078abe5c594d0
Monica J., School of Commerce, Finance and Accountancy, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Anuradha P.S., School of Commerce, Finance and Accountancy, CHRIST (Deemed to be University), Karnataka, Bangalore, India
Antecedents of Ethical Goods and Services Tax Culture among young adults - Special Reference to Maharashtra and Karnataka
Acceptance of GST Tax System; GST Evasion; GST Tax Culture; Understanding GST; Unorganised Retail Sector
Since the implementation of the Goods and Services Tax (GST) in 2017, it has become clear that this new Indian indirect tax system is here to stay. The Indian GST Council is continuously deliberating and making efforts to improve GST revenue collection at the state and central levels. The focus is now on the young adults in the country who will play a vital role in shaping the future of GST compliance. Their tax mentality and behaviour in contributing to GST revenue as daily consumers will determine the ethical tax culture in India. They need to understand how crucial their role is in discouraging evasive practices by sellers in the unorganised retail sector at the point of sale. The study utilized structural equation modelling to test the acceptability of the model. The process was supported by a structured questionnaire, with 324 respondents between the age group of 17-30 years. Understanding GST significantly influences acceptance of GST as a tax system, however, the acceptance of the GST tax system does not significantly lead to young adults discouraging the evasive behaviour of sellers in the unorganised retail sector at the point of sale. And, finally, the discouragement of evasive behaviour by young adults does influence the possibility of an ethical GST tax culture. The respondents majorly represented young adults between 17-20 years of age. The model has not measured the existence of covariance among the variables, nor has any mediating or moderating factors been identified, as GST tax culture in the Indian context is still unexplored and GST in itself is relatively new in the country. 2024 IEEE.
TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
Monica L., School of Business and Management, Christ (Deemed to Be University), Pune, India; Parameswaran S., School of Business and Management, Christ (Deemed to Be University), Pune, India
Artificial intelligence and service marketing innovation
The integration of artificial intelligence (AI) into service marketing in India is expected to significantly impact marketing strategies and economic dynamics. The emphasis on personalization, automation, predictive analytics, and chatbots will enhance customer engagement and brand loyalty, leading to increased sales and revenue. Automation of marketing workflows will streamline operations, improve efficiency, and foster business growth. AI's predictive analytics capabilities will help businesses make informed decisions about their marketing strategies, particularly in a diverse market like India. AI-driven chatbots will enhance customer satisfaction and engagement, contributing to positive brand perception and loyalty. However, there may be concerns about job displacement, particularly in routine tasks. The growth of AI-driven service marketing can contribute to the development of a technologydriven ecosystem in India, attracting investments, fostering entrepreneurship, and stimulating innovation. 2024 by IGI Global. All rights reserved.
AI Innovation in Services Marketing, pp. 150-172.
Monica R., Christ University, India; Soju A.V., Christ University, India; Kumar B.S., Christ University, India
Face and Emotion Recognition from Real-Time Facial Expressions Using Deep Learning Algorithms
Deep learning; Emotion recognition; Face recognition; Neural networks
Emotions are faster than words in the field of humancomputer interaction. Identifying human facial expressions can be performed by a multimodal approach that includes body language, gestures, speech, and facial expressions. This paper throws light on emotion recognition via facial expressions, as the face is the basic index of expressing our emotions. Though emotions are universal, they have a slight variation from one person to another. Hence, the proposed model first detects the face using histogram of gradients (HOG) recognized by deep learning algorithms such as linear support vector machine (LSVM), and then, the emotion of that person is detected through deep learning techniques to increase the accuracy percentage. The paper also highlights the data collection and preprocessing techniques. Images were collected using a simple HAAR classifier program, resized, and preprocessed by removing noise using a mean filter. The model resulted in an accuracy percentage for face and emotion being 97% and 92%, respectively. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes on Data Engineering and Communications Technologies, Vol-114, pp. 451-460.
Springer Science and Business Media Deutschland GmbH
Monica S., CHRIST (Deemed to be University), Bangalore, India; Roseline Mary R., CHRIST (Deemed to be University), Bangalore, India
Economic Burden and Productivity Loss of Employees with Lifestyle Diseases in Sedentary Occupations During Pandemic
Over the past few decades, the prevalence of Lifestyle Diseases or Non-Communicable Diseases (NCDs) have increased. There has been an increasing concern about these lifestyle diseases, with hypertension acting as the most prevalent lifestyle disease in the populace. It further exaggerates the issue as its prevalence increases exposure to other lifestyle diseases such as Diabetes and Cardiovascular Diseases (CVD). With health being an important component of human capital, the presence of lifestyle diseases has an economic impact on the individual and the organisation. The presence of an illness reduces the productivity level delivered by the individual to work, resulting in productivity loss. Apart from impacting an employee's productivity, the prevalence of lifestyle diseases incurs a significant monetary expense in the form of healthcare required to manage them. This monetary expense is called an economic burden or out-of-pocket expenditure. On these grounds, the current study examines the economic burden and impact on the productivity of employees suffering from lifestyle diseases (Hypertension, Diabetes and CVD) working in sedentary occupations. With lifestyle diseases majorly influenced by the lifestyle patterns of an individual, employees working in a sedentary occupation are at greater exposure to lifestyle diseases and hence were selected as the target population. A cross-sectional study was conducted among 426 employees of sedentary occupations in the Delhi-NCR region. The economic burden has been measured as a sum of the direct and indirect costs of the diseases incurred in a year. Using the estimates of economic burden, Catastrophic Healthcare Expenditure (CHE) was measured at different threshold levels. The study has also evaluated productivity loss through presenteeism and absenteeism approaches. An attempt was made to examine the relationship between the economic burden 7 and productivity loss through presenteeism and absenteeism approaches. The result of the study shows a significant share of the economic burden for lifestyle diseases and their comorbidities. CHE was highest at the 40% threshold level. The level of disparity in catastrophe among lower and high-income individuals was also highest at the 40% threshold level. Further statistical results show a high cost of absenteeism due to lifestyle diseases compared to presenteeism and found that economic burden has a strong positive relationship with absenteeism and presenteeism. Overall, the study concludes that lifestyle disease incurs a substantial economic burden and CHE for employees working in sedentary occupations. The estimate for the same increases if multiple lifestyle diseases are present. Further, the impact of catastrophe is more for low-income than high-income individuals due to the limited availability of resources to manage the health issue. Apart from causing monetary expense, the presence of lifestyle diseases also causes a high cost of absenteeism and presenteeism, increasing the economic cost of managing lifestyle diseases.
Hybrid Bidirectional GRU Approach for Crop Yield Prediction and Climate Change Impact Assessment in Agriculture
Analysis of Variance (ANOVA); Crop Yield (CY); Pearson Correlation Coefficient (PCC); Pesticides Tonnes (PT); Rain Fall (RF)
The impacts of climate change induced by humans will be felt most acutely by the agriculture sector due to its extreme dependence on weather. To ensure a steady supply of food, it is necessary to study and anticipate the effects of climate change on agricultural output. The impact of climate change on agricultural yield predictions is examined in this study using a novel methodology. In the proposed model, preprocessing, feature extraction, and training are the main processes. Data pretreatment guarantees quality by cleaning and normalising the data, while the PCC is utilised for feature selection. The model utilises AM and BiGRU for usage with large datasets. Using word vectors, the word embedding layer improves contextual awareness. Experiment findings show that the model is accurate to within 98.31% and can withstand a wide range of climate conditions. Current state-of-the-art methods are vastly outperformed by it, with performance measures like as R2 = 0.921%, MAE = 0.127%, and RMSE = 0.158%. These findings show that agricultural strategists and lawmakers can use AM-BiGRU to assess the effects of climate change and build a more resilient food system. 2025 IEEE.
Monika, M.; Kumar, Arun; Kelwade, Kamlesh; John, Tegil J; Krishna Prasad, S.; Nerlekar, Tanaya
Proceedings of 8th International Conference on Computing Methodologies and Communication, ICCMC 2025;pp.1458-1463
https://doi.org/10.1109/ICCMC65190.2025.11140711 https://www.scopus.com/pages/publications/105016903358?origin=resultslist
Monika M., Cambridge Institute of Technology, Department of Mca, Karnataka, Bangalore, India; Kumar A., Medicaps University, Department of Computer Science and Engineering, Madhya Pradesh, Indore, India; Kelwade K., Anjuman College of Engineering and Technology, Department of Computer Science and Engineering, Maharashtra, Nagpur, India; John T.J., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Krishna Prasad S., Nitte (Deemed to Be University), Nmam Institute of Technology, Nitte, Department of Mechanical Engineering, Udupi, India; Nerlekar T., Dr. D. Y. Patil Institute of Technology, Department of Civil Engineering, Pimpri, Maharashtra, Pune, India
Digital Transaction Cyber-Attack Detection Using Particle Swarm Optimization
Cyber-attacks; Deep Learning; Digital Transaction; Genetic Algorithm; Machine Learning; Particle Swarm Optimization
The cyber digital world is an essential variant in day-to-day life in advanced technology. There is a better change in the lifestyle as intelligent technology. In larger excite to increase the advanced technology which can be developed to humans in major dependent on network and internet users. Now, in modern times, the internet has changed the primary need in human lifestyle by giving access to everything in the world while sitting in one place knowing and updating the information and usage of online subscribers or Revolution. The world is moving in Rapid and Faster communications within a fraction of a second, at a lesser cost, and it has minimal paper-based processes and relies on the digitization document instead of a paperless environment. The data is handled by finch security practices, which are used in security worldwide to establish protected data management systems like digital lending, credits, mobile Banking, and mobile payment. Cryptocurrency and blockchain, B-trading, and banking as a service are included. At the same time, leveraging the new technologies is to resist hacking cyber-attacks. This article is also involved in artificial intelligence and machine learning (AI&ML) in different cyber-attacks. This article focuses on genetic algorithms to detect the cyber-attack. The main aim of the detection is future to prevent these cyber-attacks. The comparison will take two sample genetic algorithms. The first one is taken for Ant Colony Optimization (ACO), and the proposed model is taken for Particle Swarm Optimization. The average attack detection of ACO algorithm is 45 packets at the same time PSO algorithm will detect 50 packets. 2023 IEEE.
Proceedings of the 4th IEEE International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023
https://doi.org/10.1109/ICSTCEE60504.2023.10585244 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199521985&doi=10.1109%2fICSTCEE60504.2023.10585244&partnerID=40&md5=14e5f9ec60fd6765d1dc5f1c63aef8a9
Monisha N., Christ (Deemed To Be University), Department Of Cse, India; Gunvanth G., Christ (Deemed To Be University), Department Of Civil Engineering, India; Jayapandian N., Christ (Deemed To Be University), Department Of Cse, India
Structural Health Monitoring Using Machine Learning Techniques
Activation function; Artificial Neural Networks; Decision Tree; K-Nearest Neighbor; Logistic Regression; Machine learning algorithms; Random Forest
Environmental factors, particularly vibrations and temperature can damage the structural health of the building. To avoid heavy damage to the building and to maintain the building's structural health this paper suggests monitoring of building using machine learning algorithms. Machine learning algorithms are used to predict temperature and vibration damages in buildings. Temperature and vibration values are obtained through the grove vibration sensor and NTC thermistor attached to Raspberry Pi 3B plus. In the Raspberry pi, Machine learning algorithms are executed. The activation functions used are Relu, Sigmoid, and Tanh. The experimental results reveal that the Sigmoid activation function gives the best results in terms of metrics with accuracy 94.25, Precision 0.951, Recall 0.912, and F1 score 0.388. The sigmoid function is used in machine learning algorithms for predicting temperature and vibrations. Predicted temperature and vibrations damages are sent to the server and viewed through the user mobile. K- Nearest Neighbor algorithm produced best results with an accuracy rate of 85.50, Precision of 0.922, Sensitivity of 0.830, Specificity of 0.840 and F1 score of 0.873. 2023 IEEE.
Monisha P.; Arunraja A.; Judeson Antony Kovilpillai J.
Proceedings - 2023 3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023, pp. 275-282.
https://doi.org/10.1109/ICPCSN58827.2023.00052 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175235164&doi=10.1109%2fICPCSN58827.2023.00052&partnerID=40&md5=280b3f33e5dbc6c6fecaee370cc44dcc
Monisha P., Sri Ramakrishna Engineering College, Department of Electronics and Communication Engineering, Coimbatore, India; Arunraja A., Christ University (Deemed to Be University), School of Engineering and Technology, Department of Electronics and Communication Engineering, Bengaluru, India; Judeson Antony Kovilpillai J., Sri Ramakrishna Engineering College, Department of Electronics and Communication Engineering, Coimbatore, India