283
20
16481
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Book Chapter
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Economic Sustainability, Mindfulness, and Diversity in the Age of Artificial Intelligence and Machine Learning
Description
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The sustainability of artificial intelligence (Al) and machine learning (ML) requires human diversity and mindfulness. This chapter discusses the various ways in which AI and ML can interact with humans to improve society, e.g., in filing copyrights or design patents or increasing mindfulness. AI and ML could educate weavers and farmers about their legal rights, cultivation methods, banking processes, and the harmful effects of tobacco consumption and other health-related issues. AI and ML could help teach mindfulness. ML can measure additional biofeedback. Music, mathematics, and art may benefit from AI and machine learning. Human-technology relations and the blue-green deployment model can be used to maintain two independent infrastructures or duplicate feature stores. It is possible to cultivate mindfulness and an awareness of diversity and communal harmony through AI and machine learning, as AI and machine learning can infer the emotional and cognitive states of the people with whom they interact. By leveraging the entire process of visualization, reading, and listening with AI, machine learning, and beyond, the digital future has the potential to incorporate real-time emotions and feelings. This would entail emotional responses on both ends and a variety of other technologies and users. 2024 Taylor & Francis Group, LLC.
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Singha R.; Singha S.
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Machine Intelligence: Computer Vision and Natural Language Processing, pp. 273-285.
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CRC Press
Date
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2023-01-01
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<a href="https://doi.org/10.1201/9781003424550-15" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003424550-15</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173369972&doi=10.1201%2F9781003424550-15&partnerID=40&md5=dddfa945244411b4a271792e5e66f11d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173369972&doi=10.1201%2f9781003424550-15&partnerID=40&md5=dddfa945244411b4a271792e5e66f11d</a>
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ISBN: 978-100096031-0; 978-103220199-3
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Online
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English
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Book chapter
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Singha R., Department of Psychology, Christ University, Karnataka, Bangalore, India; Singha S., Department of Commerce, Christ University, Karnataka, Bangalore, India, Department of Commerce, Kristu Jayanti College, Karnataka, Bangalore, India
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Faculty Publications
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Channappaji, Spurthi N.; Shivappa, Bindhya Billavara; Prakash, Akhil
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Economic, Political, and Demographic Drivers of Social Isolation: Exploring the Role of Digital Literacy and Migration in Shaping Social Isolation - A Qualitative Study
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01-01-2025
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Economic, Political, and Demographic Drivers of Social Isolation;pp.223-256
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<a href="https://doi.org/10.4018/979-8-3373-5811-6.ch007" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-5811-6.ch007</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105040323717?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105040323717?origin=resultslist</a>
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Channappaji S.N., Christ University, India; Shivappa B.B., Christ University, India; Prakash A., Christ University, India
Description
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This chapter examines the impact of migration and digital literacy on social isolation amongst workers. Migration can disrupt established social networks, making it challenging for an individual to establish and build new connections. The research employed a qualitative approach, and data were collected through semi-structured interviews with migrant workers residing in Bengaluru. The findings provide contextual information on the causes of social isolation and help acquire more knowledge on how migration and digital literacy relate and influence social isolation. It prioritises individual experience over statistical data, with an increased understanding of the drivers of social isolation. Advanced digital literacy, on the other hand, can reduce social isolation by enabling migrants to maintain connections with their immediate family, access information, and develop innovative social networks. The research study's findings had a significant impact on policies and employers, highlighting the importance of social integration and mental health. Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development.
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IGI Global Scientific Publishing
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ISBN: 979-833735813-0; 979-833735811-6; 979-833735812-3;
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English
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Book chapter
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Faculty Publications
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Johnson, Deepak; Modak, Tapas Singh
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Economics of Farming in Mahatwar, Uttar Pradesh
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01-01-2025
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Review of Agrarian Studies;Volume;15;Issue;2;pp.71-87
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<a href="https://doi.org/10.25003/RAS.15.02.0006" target="_blank" rel="noreferrer noopener">https://doi.org/10.25003/RAS.15.02.0006</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029486323?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029486323?origin=resultslist</a>
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Johnson D., Department of Economics, Christ University, India; Modak T.S., Foundation for Agrarian Studies, India
Description
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Recent policy efforts have focussed on transforming eastern Uttar Pradesh, an acknowledgement of the relative backwardness of the regions agricultural development. Despite this, there has been little discussion in the literature of agrarian relations and their implications for the economics of farming. Taking Mahatwar village in eastern Uttar Pradesh as a case study, this article examines disparities across socio-economic classes in incomes and the costs of cultivation. We found substantial inequality, with landlord and big capitalist farmer households earning nearly 30 times the annual income of lower peasant and manual worker households. These disparities arise primarily from differences in costs: poor peasant and manual worker households bear a disproportionate rental burden, rely excessively on family labour, and use much of their produce for self-consumption. Our findings highlight the need for rent reduction and yield enhancement, along with support measures such as minimum support prices (MSPs), to provide meaningful incomes to low-income farmers. 2025, Tulika Books. All rights reserved.
Subject
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cost of cultivation; crop incomes; Eastern Uttar Pradesh; minimum support price (MSP); rent; socio-economic classes; yield
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Tulika Books
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ISSN: 22494405;
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English
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Article
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Format
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online
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Title
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Articles
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Title
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Econophysical bourse volatility-Global Evidence
Subject
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Bubble; Econophysics; Financial Reynolds number; Herding; volatility
Description
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Financial Reynolds number (Re) has been proven to have the capacity to predict volatility, herd behaviour and nascent bubble in any stock market (bourse) across the geographical boundaries. This study examines forty two bourses (representing same number of countries) for the evidence of the same. This study finds specific clusters of stock markets based on embedded volatility, herd behaviour and nascent bubble. Overall the volatility distribution has been found to be Gaussian in nature. Information asymmetry hinted towards a well-discussed parameter of 'financial literacy' as well. More than eighty percent of indices under consideration showed traces of mild herd as well as bubble. The same indices were all found to be predictable, despite being stochastic time series. In the end, financial Reynolds number (Re) has been proved to be universal in nature, as far as volatility, herd behaviour and nascent bubble are concerned. 2020 Bikramaditya Ghosh et al., published by Sciendo 2020.
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Ghosh B.; Mc K.
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Journal of Central Banking Theory and Practice, Vol-9, No. 2, pp. 87-107.
Publisher
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Sciendo
Date
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2020-01-01
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<a href="https://doi.org/10.2478/jcbtp-2020-0015" target="_blank" rel="noreferrer noopener">https://doi.org/10.2478/jcbtp-2020-0015</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088035656&doi=10.2478%2Fjcbtp-2020-0015&partnerID=40&md5=0c4ee0078de671adbc41ef3854b9bb23" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088035656&doi=10.2478%2fjcbtp-2020-0015&partnerID=40&md5=0c4ee0078de671adbc41ef3854b9bb23</a>
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All Open Access; Gold Open Access
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ISSN: 18009581
Format
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Online
Language
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English
Type
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Article
Coverage
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Ghosh B., Institute of Management, Christ University, Bangalore, India; Mc K., Institute of Management, Christ University, Bangalore, India
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Book Chapter
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Ecotourism a Sustainable Development Approach: A Case Study of Bandipur Forest
Subject
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Ecotourism; Sustainable development
Description
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Bandipur Tiger Reserve is geographically speaking, it is an ecological confluence since the Western and Eastern Ghats intersect here, making this region unique and exceptional in terms of its flora and fauna. The community land areas of all the border settlements as well as the nearby notified and unnotified forests have been included in the buffer of this tiger reserve. The scrub jungle along the park's eastern boundaries is made up of stunted trees, scattered bushes, and open grassland patches. The Eco-tourism activity is run in the two Ranges of Bandipur (54 km2) and GS Betta (28 km2), covering a total area of 82.00 km2, or around 9.40% of the Reserve's total size. From the above analysis, it could be concluded that the government should provide that there are administrative facilities, halting facilities, etc. just next to National Highway 67, which cuts through the eco-tourism region. Additionally, the village community people agree that the regions where some Private Tourist Resorts have situated border the Kundu Range's Eco-tourism area. The Reserve benefits from having almost year-round operations. The usual methods of stopping poaching, such as arresting and prosecuting offenders, have obviously failed; conservation education aiming at altering local attitudes will greatly reduce the ongoing threats to the integrity of biological systems in the Bandipur forest. Operationalizing sustainable ecotourism within protected areas ultimately relies on management and operations that maximize the industry's potential positive advantages while minimizing its negative ones. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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Rajan P.R.; Rishikesh K.B.
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Studies in Systems, Decision and Control, Vol-545, pp. 69-82.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/978-3-031-65203-5_7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-65203-5_7</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202524255&doi=10.1007%2F978-3-031-65203-5_7&partnerID=40&md5=6dfcd670a855fa564f3e9ef3b6cc3db6" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202524255&doi=10.1007%2f978-3-031-65203-5_7&partnerID=40&md5=6dfcd670a855fa564f3e9ef3b6cc3db6</a>
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ISSN: 21984182
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Online
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English
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Book chapter
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Rajan P.R., D. Y. Patil Institute of Management Studies Affiliated to Savitri Phule Pune University, Pune, India, Department of Economics, Zamorins Guruvayurappan College, Calicut University, Kozhikode, India; Rishikesh K.B., Department of Commerce, School of Commerce, Finance and Accountancy, CHRIST (Deemed to be University), Bangalore, 560029, India, Bharathiyar University, Coimbatore, India
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Faculty Publications
Conference Paper
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Gangrade, Priyanshi; Kushwah, Virendra Singh; Date, Saroj S.; Jagdale, Archana D.; Upreti, Kamal; Singh, Vinai K.
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Edge and Fog Computing in Cyber-Physical Systems
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01-01-2025
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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025;pp.172-176
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<a href="https://doi.org/10.1109/IC363308.2025.10956222" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC363308.2025.10956222</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105003902768?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105003902768?origin=resultslist</a>
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Gangrade P., School of Cse and Ai, Vit Bhopal University, Sehore, India; Kushwah V.S., School of Cse and Ai, Vit Bhopal University, Sehore, India; Date S.S., Csmss Chh. Shahu college of Engineering, Dept. of Ai and Ds, Maharashtra, Chh. Sambhajinagar, India; Jagdale A.D., Barclays Services Corp, 745, Seventh Avenue, New York, United States; Upreti K., Department of Computer Science, Christ University Delhi Ncr, Ghaziabad, India; Singh V.K., Gl Bajaj Group of Institutions, Department of Computer Science & Engineering, Mathura, India
Description
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The benefits of cyber-physical system advances include low latency and high bandwidth data processing in areas such as automotive, healthcare, and business automation. Traditional environments are often located in centralized and remote locations and cannot meet the demand. Edge computing and cloud computing have become fundamental concepts that will bring computing closer to the center of the data. Edge computing can reduce latency and bandwidth consumption by processing data on or near IoT devices. Fog computing adds another layer to this by distributing work and storage across multiple nodes, thus providing a scalable and flexible infrastructure. This article discusses the principles, benefits, and challenges of integrating edge and cloud computing into a CPS environment. It leverages the power of proximity-based edge computing and the centralized capabilities of cloud computing to provide scalable, instantaneous responses to CPS applications or time to optimize services. The demonstration shows a variety of things from smart cities to the use of IoT in healthcare in CPS. The article also covers some specific security and privacy issues and future directions in distributed computing, including the role of AI and 5G, which are supposed to offer additional resources in various applications. 2025 IEEE.
Subject
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Cyber-Physical Systems (CPS); Data Processing; Edge Computing; Fog Computing
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833152749-5;
Language
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English
Type
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Conference paper
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-
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Faculty Publications
Conference Paper
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Roy, Santanu; Suresh, Ashvath; Adhikari, Prashant; Gupta, Archit
Title
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Edge Attention Module for Object Classification
Date
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01-01-2025
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Proceedings of the International Joint Conference on Neural Networks;
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<a href="https://doi.org/10.1109/IJCNN64981.2025.11227631" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IJCNN64981.2025.11227631</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105023977072?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105023977072?origin=resultslist</a>
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Roy S., Dept. of Computer Science, NIIT University, Jaipur, India; Suresh A., Dept. of Computer Science and Engg, Christ (Deemed to be University), Bangalore, India; Adhikari P., Dept. of Computer Science, Christ (Deemed to be University), Bangalore, India; Gupta A., Dept. of Computer Science, NIIT University, Jaipur, India
Description
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A novel edge attention-based Convolutional Neural Network (CNN) is proposed in this research for object classification task. With the advent of advanced computing technology, CNN models have achieved to remarkable success, particularly in computer vision applications. Nevertheless, the efficacy of the conventional CNN is often hindered due to class imbalance and inter-class similarity problems, which are particularly prominent in the computer vision field. In this research, we introduce for the first time an Edge Attention Module (EAM) consisting of a Max-Min pooling layer, followed by convolutional layers. This Max-Min pooling is entirely a novel pooling technique, specifically designed to capture only the edge information that is crucial for any object classification task. Therefore, by integrating this novel pooling technique into the attention module, the CNN network inherently prioritizes on essential edge features, thereby boosting the accuracy and F1-score of the model significantly. We have implemented our proposed EAM or 2EAMs on several standard pre-trained CNN models for Caltech-101, Caltech-256, CIFAR-100 and Tiny ImageNet-200 datasets. The extensive experiments reveal that our proposed framework (that is, EAM with CNN and 2EAMs with CNN), outperforms all pre-trained CNN models as well as recent trend models Pooling-based Vision Transformer (PiT), Convolutional Block Attention Module (CBAM), and ConvNext, by substantial margins. We have achieved the accuracy of 95.5% and 86% by the proposed framework on Caltech-101 and Caltech-256 datasets, respectively. So far, this is the best results on these datasets, to the best of our knowledge. All the codes along with graphs, and their classification reports are shared on an anonymous GitHub link: https://anonymous.4open.science/r/Object-Classification-7BE5. 2025 IEEE.
Subject
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Convolutional Neural Network (CNN); Edge Attention Module (EAM); Max-Min Pooling; Vision Transformer (ViT)
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Institute of Electrical and Electronics Engineers Inc.
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A related resource
ISSN: 21614393; ISBN: 979-833151042-8; CODEN: 85OFA
Language
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English
Type
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Conference paper
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Restricted Access; Hardcopy may be available in the library
-
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Faculty Publications
Conference Paper
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Radhakrishnan, P.; Shravani, K.; Prasadula, Naveen; Maheshwari, Sumit; Acharjee, Purnendu Bikash; Dhanraj, Joshuva Arockia
Title
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Edge Computing and Real-Time Analytics as the Next Frontier for Big Data in IT Companies
Date
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01-01-2025
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Proceedings - 2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies, SISIMPACT 2025;pp.985-990
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<a href="https://doi.org/10.1109/SISIMPACT67725.2025.11439636" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/SISIMPACT67725.2025.11439636</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037433242?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037433242?origin=resultslist</a>
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Radhakrishnan P., School of CS and AI, SR University, Telangana, Warangal, 506371, India; Shravani K., Malla Reddy University, Department of Data Science, Telangana, Hyderabad, 500100, India; Prasadula N., Pannala Ram Reddy College of Business Management, Osmania University, Hyderabad, India; Maheshwari S., ABES Engineering College, Uttar Pradesh, Ghaziabad, India; Acharjee P.B., Computer Science CHRIST University, Bengaluru, India; Dhanraj J.A., University Centre for Research and Development (UCRD), Chandigarh University, Punjab, Mohali, 140413, India
Description
An account of the resource
Edge computing combined with real-Time analytics is rapidly transforming the way the IT companies can use big data to make smarter and quicker decisions. Centralized model of clouds traditional clouds are being put under stress owing to an explosive increase in data volume, latency sensitive applications as well as bandwidth limitations. Edge computing also makes computation much closer to data sources and allows real-Time analytics that can mitigate latency by orders of magnitude, increase data security and achieve instant insights at a scale. This paradigm shift gives power to IT firms to maximize operations, individualize services and allow agile reactions in a dynamic scenario like smart infrastructure, IoT implementations, and AI-based systems. Edge computing can also be used to offload processing to the edge thus reducing traffic in the core network as well as enabling distributed intelligence. Moreover, edge systems, coupled with AI models, make IT infrastructures perform predictive analytics at the source and become less dependent on backhaul links. This hybridizing process is a paradigm shift in the serious research of big data strategies, and in a future where competitive advantage will rest on latency, context-sensitivity, and localized smarts. 2025 IEEE.
Subject
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Big data; Decentralized processing; Edge computing; Latency; Real-Time analytics
Publisher
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Institute of Electrical and Electronics Engineers Inc.
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A related resource
ISBN: 979-833155787-4;
Language
A language of the resource
English
Type
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Conference paper
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Restricted Access; Hardcopy may be available in the library
-
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Book Chapter
Book Chapter
Faculty Publications- Book Chapter
Dublin Core
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Title
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Edge computing for smart disease prediction treatment therapy
Subject
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Aautomated medical diagnosis systems; Cardio-vascular diseases; Deep learning; Edge computing; Healthcare system; Machine learning; Sensors; Smart devices; Surveillance frameworks
Description
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Healthcare systems are increasingly seeking to match patients' pace of life and be personalized, as they are demanding more advanced products and services. The only solution for collecting and analyzing health data in realtime is an edge computing (EC) environment, coupled with 5G speeds and modern computing techniques. The technology in healthcare is currently being used to develop smart systems that can expedite the diagnosis of disease and provide precise and timely treatment. The automated hospital monitoring system and medical diagnosis system enable doctors to monitor and diagnose patients from a variety of locations, including hospitals, workplaces, and homes and provide transportation options. As a result, overall doctor visits are reduced as well as patient care is improved. More than 162 billion healthcare IoT devices are expected to be used worldwide by 2021 thanks to the internet of things (IoT) sensors and applications for general healthcare. With edge intelligence (EI), wearable devices with sensors, like smartwatches or smartphones, and gateway devices, such as microcontrollers, can form edge nodes: smart devices with sensors, as well as gateway devices with sensors, can act as edge nodes. Smart sensor devices are typically installed at a greater distance from personal computers (PCs) and servers, which can be utilized in fog computing (FC). In healthcare, EC and FC are used to deliver reliable, low-latency, and location-aware healthcare services by utilizing sensors located within users' reach. Recently, many researchers have proposed using hierarchical computing for the distribution and allocation of inference-based tasks among edge devices and fog nodes, which could lead to an increase in computing power and compute capability of edge devices. For disease prediction, this chapter discusses a variety of EC techniques. 2024 Apple Academic Press, Inc. All rights reserved.
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Nanjundan P.; Jaisingh W.; George J.P.
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Reconnoitering the Landscape of Edge Intelligence in Healthcare, pp. 89-114.
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Apple Academic Press
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2024-01-01
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<a href="" target="_blank" rel="noreferrer noopener"></a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185094359&partnerID=40&md5=21c020c5c7134f5c43794d07b3ef1726" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185094359&partnerID=40&md5=21c020c5c7134f5c43794d07b3ef1726</a>
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Restricted Access
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ISBN: 978-100089493-6; 978-177491436-6
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Online
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English
Type
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Book chapter
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Nanjundan P., Department of Data Science, Christ University, Lavasa, Pune, Maharashtra, India; Jaisingh W., School of Computing Science and Engineering, VIT Bhopal University, Bhopal, Madhya Pradesh, India; George J.P., Department of Data Science, Christ University, Lavasa, Pune, Maharashtra, India
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Book Chapter
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Faculty Publications- Book Chapter
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Title
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Edge Computing in Aerial Imaging A Research Perspective
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Aerial imaging; Edge computing; Internet of drones
Description
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Internet of Drones (IoD) is a field that has a vast scope for improvement due to its high adaptability and complex problem statements. Aerial vehicles have been employed in various applications such as rescue operations, agriculture, crop productivity analysis, disaster management, etc. As computing and storage power have increased, satellite imaging and drone imaging have become possible, with vast datasets available for study and experiments. The recent work lies in the edge computing sector, where the captured aerial images are processed at the edge. Our paper focuses on the algorithms and technologies that easily facilitate aerial image processing. The applications and their architectures are focused on which can efficiently function using aerial processing. The various research perspectives in aerial imaging are concentrated on paving the way for further research. 2024 Scrivener Publishing LLC. All rights reserved.
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Vetriveeran D.; Sambandam R.K.; Jenefa J.; Leena Sri R.
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Advances in Aerial Sensing and Imaging, pp. 355-382.
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wiley
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2024-01-01
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<a href="" target="_blank" rel="noreferrer noopener"></a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192588729&partnerID=40&md5=1bcb7ac2845096a9e29ab9c937c6197a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192588729&partnerID=40&md5=1bcb7ac2845096a9e29ab9c937c6197a</a>
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ISBN: 978-139417551-2; 978-139417469-0
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Online
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English
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Book chapter
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Vetriveeran D., Department of CSE, Christ University, Karnataka, Bangalore, India; Sambandam R.K., Department of CSE, Christ University, Karnataka, Bangalore, India; Jenefa J., Department of CSE, Christ University, Karnataka, Bangalore, India; Leena Sri R., Department of CSE, Thiagarajar College of Engineering, Tamil Nadu, Madurai, India
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Faculty Publications
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Joseph, James; Joseph, Mayamma
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Edge criticality in signed graphs admitting a Roman dominating function
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01-01-2025
Source
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Art of Discrete and Applied Mathematics;
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<a href="https://doi.org/10.26493/2590-9770.1833.d92" target="_blank" rel="noreferrer noopener">https://doi.org/10.26493/2590-9770.1833.d92</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105017653688?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105017653688?origin=resultslist</a>
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Joseph J., CHRIST (Deemed to be University), Bangalore, 560029, India; Joseph M., CHRIST (Deemed to be University), Bangalore, 560029, India
Description
An account of the resource
A Roman dominating function(RDF) on a signed graph S = (G, ?) is a function f: V (S) ? {0, 1, 2} such that f(N[v]) ? 1 for every vertex v ? V (S) and any vertex v with f(v) = 0 has a neighbour u ? N + P (v) having f(u) = 2, where f(N[v]) = f(v) + ?u?N(v) ?(uv)f(u). The weight of an RDF is ?(f) = ?v?V f(v) and the minimum weight among all the RDFs on S is called the Roman domination number, ?R(S). In this article we explore the concept of edge criticality in signed graphs admitting an RDF by examining the signed graphs S such that ?R(S+uv) < ?R(S), for any pair of non-adjacent vertices u and v of S, such that the edge uv is positive. This work is licensed under https://creativecommons.org/licenses/by/4.0/
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roman dominating function; roman domination edge critical graphs; roman domination number; Signed graphs
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University of Primorska
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ISSN: 25909770;
Language
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English
Type
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Article
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All Open Access; Gold Open Access
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online
-
https://archives.christuniversity.in/files/original/259e29478e0d8a07d3758a8130305b79.pdf
0a17eb5462a36bf9e32b9b4c5aeb0322
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MPHIL
Mphil
Mphil Thesis
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Title
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Edge Domination in Certain Families of
Chemical Graphs
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Mathias Anisha Jean
Date
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2018
Source
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Mathematics
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Articles
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Title
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Edge incident 2-edge coloring of graphs
Subject
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Edge incident 2-edge coloring; edge incident 2-edge coloring number
Description
An account of the resource
The edge incident 2-edge coloring of a graph G is an edge coloring of the graph G such that not more than two colors are assigned to the edges incident to an edge e = uv in G. In other words, for every edge e in G, the edge e and all the edges that are incident to the edge e is in at most two different color classes. The edge incident 2-edge coloring number ?n2(G) is the maximum number of colors in any edge incident 2-edge coloring of G. The main objective of this paper is to study the edge incident 2-edge coloring concept and apply the same to some graph classes. Besides finding the exact values of these parameters, we also obtain some bounds. World Scientific Publishing Company.
Creator
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Joseph A.; Dominic C.
Source
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Discrete Mathematics, Algorithms and Applications
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World Scientific
Date
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2024-01-01
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<a href="https://doi.org/10.1142/S179383092450040X" target="_blank" rel="noreferrer noopener">https://doi.org/10.1142/S179383092450040X</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192953734&doi=10.1142%2FS179383092450040X&partnerID=40&md5=0a17f4c4ffa65f5343b8f39b47667d3c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192953734&doi=10.1142%2fS179383092450040X&partnerID=40&md5=0a17f4c4ffa65f5343b8f39b47667d3c</a>
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ISSN: 17938309
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Online
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English
Type
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Article
Coverage
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Joseph A., Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Dominic C., Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India
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Faculty Publications
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Faculty Publications -Articles
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Joseph, Anu; Dominic, Charles
Title
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Edge incident 2-edge coloring of graphs
Date
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01-01-2025
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Discrete Mathematics, Algorithms and Applications;Volume;17;Issue;3;Article No.;2450040;
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<a href="https://doi.org/10.1142/S179383092450040X" target="_blank" rel="noreferrer noopener">https://doi.org/10.1142/S179383092450040X</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105003015614?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105003015614?origin=resultslist</a>
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Joseph A., Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Dominic C., Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India
Description
An account of the resource
The edge incident 2-edge coloring of a graph G is an edge coloring of the graph G such that not more than two colors are assigned to the edges incident to an edge e = uv in G. In other words, for every edge e in G, the edge e and all the edges that are incident to the edge e is in at most two different color classes. The edge incident 2-edge coloring number ?'ein2 (G) is the maximum number of colors in any edge incident 2-edge coloring of G. The main objective of this paper is to study the edge incident 2-edge coloring concept and apply the same to some graph classes. Besides finding the exact values of these parameters, we also obtain some bounds. 2025 World Scientific Publishing Company.
Subject
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Edge incident 2-edge coloring; edge incident 2-edge coloring number
Publisher
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World Scientific
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ISSN: 17938309;
Language
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English
Type
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Article
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Restricted Access; Hardcopy may be available in the library
Format
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online
-
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Articles
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Title
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EDGE INCIDENT 2-EDGE COLORING SUM OF GRAPHS
Subject
The topic of the resource
Edge incident 2-edge coloring; edge incident 2-edge coloring number; edge incident 2-edge coloring sum
Description
An account of the resource
The edge incident 2-edge coloring number, ?ein2(G), of a graph G is the highest coloring number used in an edge coloring of a graph G such that the edges incident to an edge e = uv in G is colored with at most two distinct colors. The edge incident 2-edge coloring sum of a graph G, denoted as (Formula presented.), is the greatest sum among all the edge incident 2-edge coloring of graph G which receives maximum ?ein2(G) colors. The main objective of this paper is to study the edge incident 2-edge coloring sum of graphs and find the exact values of this parameter for some known graphs. I??k University, Department of Mathematics, 2025; all rights reserved.
Creator
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Joseph A.; Dominic C.
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Turkish World Mathematical Society Journal of Applied and Engineering Mathematics, Vol-15, No. 1, pp. 98-109.
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Isik University
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2025-01-01
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<a href="" target="_blank" rel="noreferrer noopener"></a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214305503&partnerID=40&md5=a6ebd9fbfe1ed51d60513e9eed878d60" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214305503&partnerID=40&md5=a6ebd9fbfe1ed51d60513e9eed878d60</a>
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Restricted Access
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ISSN: 21461147
Format
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Online
Language
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English
Type
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Article
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Joseph A., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, India; Dominic C., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, India, Department of Mathematical Sciences, University of Essex, Essex, Colchester, CO4 3SQ, United Kingdom
-
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Faculty Publications
Article
Faculty Publications -Articles
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Joseph, A.; Dominic, C.
Title
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EDGE INCIDENT 2-EDGE COLORING SUM OF GRAPHS
Date
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01-01-2025
Source
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Turkish World Mathematical Society Journal of Applied and Engineering Mathematics;Volume;15;Issue;1;pp.98-109
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<a href="https://www.scopus.com/pages/publications/85214305503?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85214305503?origin=resultslist</a>
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Joseph A., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, India; Dominic C., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, India, Department of Mathematical Sciences, University of Essex, Essex, Colchester, CO4 3SQ, United Kingdom
Description
An account of the resource
The edge incident 2-edge coloring number, ?ein2(G), of a graph G is the highest coloring number used in an edge coloring of a graph G such that the edges incident to an edge e = uv in G is colored with at most two distinct colors. The edge incident 2-edge coloring sum of a graph G, denoted as (Formula presented.), is the greatest sum among all the edge incident 2-edge coloring of graph G which receives maximum ?ein2(G) colors. The main objective of this paper is to study the edge incident 2-edge coloring sum of graphs and find the exact values of this parameter for some known graphs. I??k University, Department of Mathematics, 2025; all rights reserved.
Subject
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Edge incident 2-edge coloring; edge incident 2-edge coloring number; edge incident 2-edge coloring sum
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Isik University
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A related resource
ISSN: 21461147;
Language
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English
Type
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Article
Rights
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Restricted Access; Hardcopy may be available in the library
Format
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online
-
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Book Chapter
Book Chapter
Faculty Publications- Book Chapter
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Title
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Edge intelligence to smart management and control of epidemic
Subject
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Artificial intelligence; Computational intelligence; Edge computing; Edge COVID-19; Edge intelligence; Internet of things; Smart healthcare
Description
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The effects of COVID-19 vary from person to person. A pandemic is devastating economically and socially. Thousands of enterprises face the possibility of collapse. More than half of the world's 3.3 billion workers may lose their livelihoods if the current crisis continues. The world's healthcare services are facing an unprecedented situation due to the recent outbreak of a novel coronavirus (COVID-19). Community and government health are adversely affected by the COVID-19 pandemic. COVID-19 has continued to spread, and mortalities have risen steadily. The spread of this disease can therefore be controlled utilizing nonpharmacological methods, such as quarantine, isolation, and public health education. Recent breakthroughs in deep learning (DL) have led to an explosion in applications and services relating to artificial intelligence (AI). The rapid advancements in mobile computing and AI have enabled zillions of Bytes of data to be generated at the network edge from thousands of mobile devices and internet of things (IoT) devices connected to the Internet. As a result of the success of IoT and AI technologies, it is of utmost importance that we expand the AI frontiers to the network edge in order for big data to be fully tapped. Edge computing (EC) can help overcome this trend because it allows computation-intensive AI applications to run on edge hardware. The topic of discussion in this chapter is edge intelligence (EI) technology's application in limiting virus spread during pandemics. 2024 Apple Academic Press, Inc. All rights reserved.
Creator
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Nanjundan P.; George J.P.; Karpagam C.
Source
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Reconnoitering the Landscape of Edge Intelligence in Healthcare, pp. 175-190.
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Apple Academic Press
Date
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2024-01-01
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<a href="" target="_blank" rel="noreferrer noopener"></a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185138985&partnerID=40&md5=bfda0d0c3c38958c5263159c0b037f91" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185138985&partnerID=40&md5=bfda0d0c3c38958c5263159c0b037f91</a>
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Restricted Access
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ISBN: 978-100089493-6; 978-177491436-6
Format
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Online
Language
A language of the resource
English
Type
The nature or genre of the resource
Book chapter
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Nanjundan P., Department of Data Science, Christ University, Lavasa, Pune, Maharashtra, India; George J.P., Department of Data Science, Christ University, Lavasa, Pune, Maharashtra, India; Karpagam C., Department of Computer Science with Data Analytics, Dr. N.G.P. Arts and Science College, Tamil Nadu, India
-
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Book Chapter
Book Chapter
Faculty Publications- Book Chapter
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Title
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Edge, IIoT with AI: Transforming industrial engineering and minimising security threat
Description
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[No abstract available]
Creator
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Jeyalakshmi J.; Rohan K.; Samuel P.; Berna I.E.; Vijay K.
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Industrial Internet of Things Security: Protecting AI-Enabled Engineering Systems in Cloud and Edge Environments, pp. 165-184.
Publisher
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CRC Press
Date
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2024-01-01
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<a href="https://doi.org/10.1201/9781003466284-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003466284-9</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205566609&doi=10.1201%2F9781003466284-9&partnerID=40&md5=9f5a9f48c64e8557b8289cc474ad44c9" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205566609&doi=10.1201%2f9781003466284-9&partnerID=40&md5=9f5a9f48c64e8557b8289cc474ad44c9</a>
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Restricted Access
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A related resource
ISBN: 978-104014675-0; 978-103273850-5
Format
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Online
Language
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English
Type
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Book chapter
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Jeyalakshmi J., Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidhyapeetham, Chennai, India; Rohan K., Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidhyapeetham, Chennai, India; Samuel P., Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattankulathur Campus, Chennai, India; Berna I.E., Department of Artificial Intelligence and Machine Learning, Bannari Amman Institute of Technology, Erode, India; Vijay K., Department of Statistics and Data Science, CHRIST (Deemed to be University), Bengaluru, India
-
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Book Chapter
Book Chapter
Faculty Publications- Book Chapter
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Edge/Fog Computing: An Overview and Insight into Research Directions
Description
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The rapid proliferation of data from applications including IoT, and on-demand access to data have increased dependency on cloud computing, which helps to minimize the overhead related to data storage and maintenance. Applications such as IoT, industrial control, etc. generate data which are highly time-critical in most scenarios. The cloud platform offers permanent storage of this massive amount of data but with comparatively less focus on time-sensitivity. Edge/fog computing are extensions of the cloud computing paradigm and require less response time for time-sensitive data. The edge/fog brings processing and storage closer to the edge of the network, thereby reducing network traffic, delay, and latency. It acts as an intermediate layer between the end devices and the cloud platform, for data collection, offloading, processing, and data management. This chapter addresses the need for fog computing, presents the design model for edge/fog computing, and discusses applications and open issues of implementation. The three-layered network model, the services provided by the edge/fog computing, and a few research challenges of implementation will also be discussed. 2024 Taylor & Francis Group, LLC.
Creator
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Thomas P.; Jose D.V.
Source
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Machine Intelligence: Computer Vision and Natural Language Processing, pp. 47-64.
Publisher
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CRC Press
Date
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2023-01-01
Identifier
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<a href="https://doi.org/10.1201/9781003424550-3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003424550-3</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173352312&doi=10.1201%2F9781003424550-3&partnerID=40&md5=6a4e67739a0d73ce560024b5f5b056b0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173352312&doi=10.1201%2f9781003424550-3&partnerID=40&md5=6a4e67739a0d73ce560024b5f5b056b0</a>
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Restricted Access
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ISBN: 978-100096031-0; 978-103220199-3
Format
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Online
Language
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English
Type
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Book chapter
Coverage
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Thomas P., Department of Computer Science, Christ University, Bangalore, India; Jose D.V., Department of Computer Science, Christ University, Bangalore, India
-
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Title
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Faculty Publications
Book Chapter
Faculty Publications- Book Chapter
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
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Murthy, Hosakatte Niranjana; Yadav, Guggalada Govardhana; Paek, Kee Yoeup; Park, So-Young
Title
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Edible Flowers: An Updated Review on Nutritional Composition, Phytochemicals, and Biological Activities
Date
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01-01-2026
Source
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Bioactive Compounds in Edible Flowers;pp.3-34
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<a href="https://doi.org/10.1201/9781003645979-2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003645979-2</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029214254?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029214254?origin=resultslist</a>
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Murthy H.N., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, 560029, India, Department of Horticultural Science, Chungbuk National University, Cheongju, 28644, South Korea, Department of Botany, Karnatak University, Dharwad, 580003, India; Yadav G.G., Department of Botany, Karnatak University, Dharwad, 580003, India, Department of Botany, ADB First Grade College, Harapanahalli, 583131, India; Paek K.Y., Department of Horticultural Science, Chungbuk National University, Cheongju, 28644, South Korea; Park S.-Y., Department of Horticultural Science, Chungbuk National University, Cheongju, 28644, South Korea
Description
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Edible flowers have been identified as a possible source of vitamins, minerals, protein, fat, and carbohydrates. Additionally, they are abundant in bioactive substances such as terpenoids, alkaloids, phenolics, flavonoids, glucosinolates, and essential oils. The pharmacological properties of edible flowers have been shown to include anti-aging, anti-cancer, antidiabetic, anti-inflammatory, antimicrobial, antioxidant, cardioprotective, hepatoprotective, and neuroprotective effects in recent years. The most recent information on the nutritional, phytochemical, and biological properties of agathi, lotus, moringa, and banana flowers is presented in this review. This analysis concludes by shedding light on the latest data regarding the use of edible flowers in the food, pharmaceutical, and cosmetic industries. 2026 Hosakatte Niranjana Murthy.
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CRC Press
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ISBN: 978-104058108-7; 978-104107986-6;
Language
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English
Type
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Book chapter
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Restricted Access; Hardcopy may be available in the library