458
20
16481
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Dublin Core
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Title
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Faculty Publications
Book
Faculty Publications- Book
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Reddy, K. Jayasankara
Title
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Integrated Holistic Mental Healthcare
Date
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01-01-2025
Source
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Integrated Holistic Mental Healthcare: Bridging Minds and Technology;pp.1-261
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<a href="https://doi.org/10.1016/C2024-0-04122-7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/C2024-0-04122-7</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105026649415?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105026649415?origin=resultslist</a>
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Reddy K.J., Department of Psychology, Christ University, Karnataka, Bangalore, India
Description
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Integrated Holistic Mental Healthcare: Bridging Minds and Technology is a groundbreaking resource that unites traditional holistic practices with cutting-edge digital innovations to enhance mental health treatment. This comprehensive guide is tailored for mental health professionals seeking practical strategies to improve care efficacy and accessibility. The book begins with foundational concepts of holistic mental health and emphasizes the essential role of mindfulness and meditation as core components of effective care. It explores how technology can complement holistic practices, facilitating behavioral change and habit formation. Readers will discover how digital tools can expand access to mental healthcare through telehealth and virtual care models, while also addressing ethical considerations and stigma associated with mental health treatment. With insights into monitoring outcomes and case studies highlighting integrative approaches for specialized populations, this book equips readers to navigate the evolving mental health landscape. As it looks toward future trends, "Integrated Holistic Mental Healthcare" empowers individuals to enhance their well-being in a rapidly digitizing world, fostering a patient-centered care paradigm that honors both mind and technology. 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Publisher
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Elsevier
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ISBN: 978-044343908-7; 978-044343909-4;
Language
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English
Type
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Book
Rights
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Restricted Access; Hardcopy may be available in the library
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Dublin Core
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Title
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Articles
Article
Faculty Publications -Articles
Dublin Core
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Title
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Integrated Home-Based Palliative Care in Motor Neuron Disease: A Case Report from Low- Middle Income Country
Subject
The topic of the resource
Care manager; case report; home-based palliative care; low-middle income country; motor neuron disease
Description
An account of the resource
In many international care guidelines, multidisciplinary palliative care forms a key to optimum management in Motor Neuron Disease (MND). We describe the home-based palliative care interventions for a client with MND and his family from a Low and Middle-income country context. This report also discusses the advantages and challenges of the same with suggestions for sustaining the quality of care for neuro palliative conditions. 2021 Taylor & Francis Group, LLC.
Creator
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Warrier M.G.; Sadasivan A.; Johnson J.; Vengalil S.; Nalini A.; Thomas P.T.
Source
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Journal of Social Work in End-of-Life and Palliative Care, Vol-17, No. 4, pp. 262-266.
Publisher
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Routledge
Date
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2021-01-01
Identifier
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<a href="https://doi.org/10.1080/15524256.2021.1938344" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/15524256.2021.1938344</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108357913&doi=10.1080%2F15524256.2021.1938344&partnerID=40&md5=ee34c3b3fb86011c80d4ef0637893608" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108357913&doi=10.1080%2f15524256.2021.1938344&partnerID=40&md5=ee34c3b3fb86011c80d4ef0637893608</a>
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Restricted Access
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A related resource
ISSN: 15524256; PubMed ID: 34152942
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
Warrier M.G., Department of Psychology, Christ University (Deemed to be University), Bengaluru, India; Sadasivan A., Department of Counselling Psychology, Sampurna Montfort College, Bengaluru, India; Johnson J., School of Medicine, Cardiff University, Cardiff, United Kingdom, Karunashraya, Bangalore Hospice Trust, Bengaluru, India; Vengalil S., Department of Neurology, NIMHANS, Bengaluru, India; Nalini A., Department of Neurology, NIMHANS, Bengaluru, India; Thomas P.T., Department of Psychiatric Social Work, NIMHANS, Bengaluru, India
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Title
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Articles
Article
Faculty Publications -Articles
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/.
Title
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Integrated hybrid membrane system for enhanced water treatment and desalination for environmental preservation
Subject
The topic of the resource
Desalination; Energy Efficiency (EE); Environment; Hybrid; Innovation; Membrane; Preservation; Water Treatment (WT)
Description
An account of the resource
Technology advancements in desalination, water treatment, and energy efficiency are crucial to preserving our planet. It is critical to find solutions for the future that save natural resources and lessen environmental damage because the freshwater shortage is getting worse, and energy demand is increasing. They face various obstacles, even though their breakthroughs are extremely important. Lot of energy can be utilized for the traditional desalination techniques, as it negatively impacts the environment. Then, the process of the existing Water Treatment (WT) are expensive and ineffective. An Integrated Hybrid Membrane System for Enhanced WT (IHMS-EWT) is a unique technique for WT and desalination was suggested in this study. The integration of many membrane procedures like nanofiltration, reverse and forward osmosis, and membrane distillation, and these will helps in facilitating the best WT and desalination methods. Due to the incorporating Renewable Energy (RE), the IHMS-EWT also demonstrates the (SWMS) Sustainable Water Management System, as it enhances the EE and thereby reducing the environmental impact. The great potential in the wide range of applications was offered by the IHMS-EWT technique. Providing the decentralized WT solutions in the remote areas, this unique approach has the ability to reduce the fresh water scarcity in the coastal areas based on the demands of the municipal, industrial and agricultural demands. The environmental sustainability throughout the lenghthy operations was ensured by the support of IHMS-EWT. It also helps in providing resilience in the crisis situations. The cost-effective evaluations, operating parameter optimization, and performance prediction of the method was enabled by employing the computational modelling. Through simulatimg different contexts, the effective configurations and operational techniques are focussed on the study for enhancing the IHMS-EWT technology.The model shift in the SWM, the IHMS-EWT technique addresses the main problems and brings one step for more secure environment. Comparing to other existing methods, Improving the water purification by 98.2 %, 94.2 % efficiency rate, the EC prediction rate of 96.2 %, the cost-effectiveness rate by 82.4 % and the performance rate by 96.7 % by the suggested IHMS-EWT model and it was demonstrated by the outcomes of the experiment. 2024 The Authors
Creator
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Alluhaidan A.S.; Prabu P.; Basheer S.; Aziz R.; Prakash P.
Source
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Desalination and Water Treatment, Vol-320
Publisher
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Elsevier B.V.
Date
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2024-01-01
Identifier
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<a href="https://doi.org/10.1016/j.dwt.2024.100722" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.dwt.2024.100722</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202018123&doi=10.1016%2Fj.dwt.2024.100722&partnerID=40&md5=0eef98a18b80084e9f99d7b686f80697" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202018123&doi=10.1016%2fj.dwt.2024.100722&partnerID=40&md5=0eef98a18b80084e9f99d7b686f80697</a>
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All Open Access; Gold Open Access
Relation
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ISSN: 19443994
Format
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Online
Language
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English
Type
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Article
Coverage
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Alluhaidan A.S., Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia; Prabu P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Basheer S., Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia; Aziz R., Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia; Prakash P., Department of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India
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https://archives.christuniversity.in/files/original/56b345a63de4234af5116a00a8631bc1.pdf
5c969da71783c60e51014ba2c4fee108
Dublin Core
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Title
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Thesis
PhD
PhD Thesis
Dublin Core
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Title
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Integrated intelligent framework for e-learning
Subject
The topic of the resource
Computer Science
Description
An account of the resource
E-learning is the primary method of learning for most learners after regular academics studies. Knowledge delivery through e-learning technologies increased exponentially over the years because of the advancement in internet and e-learning technologies. Knowledge delivery to some people would never
have been possible without the e-learning technologies. Most of the working professionals do focused studies for carrier advancement, promotion, or for improving domain knowledge. These learners can find many free e-learning web sites from the internet easily in the domain of interest. However, it is quite
difficult to find the best e-learning content suitable for their learning based on their domain knowledge level. Users spent most of the time figuring out the right content from a plethora of available content and end up learning nothing. A framework using machine learning algorithms with Random Forest Classifier
is proposed to address the issue, which classifies the e-learning content based on its difficulty levels and provides the learner the best content suitable based on the knowledge level. The framework is trained with the data set collected from
multiple popular e-learning web sites. The model is tested with real-time e�learning web site links and found that the e-contents in the web sites are recommended to the user based on its difficult levels as beginner level, intermediate level, and advanced level.
Creator
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Thomas, Benny - 1445001
Publisher
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CHRIST (Deemed to be University)
Language
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English
Type
The nature or genre of the resource
PhD
-
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Title
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PHD
PhD
PhD Thesis
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Relation
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61000164
Title
A name given to the resource
Integrated intelligent framework for e-learning
Subject
The topic of the resource
Computer Science
Description
An account of the resource
E-learning is the primary method of learning for most learners after regular academics studies. Knowledge delivery through e-learning technologies increased exponentially over the years because of the advancement in internet and e-learning technologies. Knowledge delivery to some people would never have been possible without the e-learning technologies. Most of the working professionals do focused studies for carrier advancement, promotion, or for improving domain knowledge. These learners can find many E-learning is the primary method of learning for most learners after regular academics studies. Knowledge delivery through e-learning technologies increased exponentially over the years because of the advancement in internet and e-learning technologies. Knowledge delivery to some people would never have been possible without the e-learning technologies. Most of the working professionals do focused studies for carrier advancement, promotion, or for improving domain knowledge. These learners can find many free e-learning web sites from the internet easily in the domain of interest. However, it is quite difficult to find the best e-learning content suitable for their learning based on their domain knowledge level. Users spent most of the time figuring out the right content from a plethora of available content and end up learning nothing. A framework using machine learning algorithms with Random Forest Classifier is proposed to address the issue, which classifies the e-learning content based on its difficulty levels and provides the learner the best content suitable based on the knowledge level. The framework is trained with the data set collected from multiple popular e-learning web sites. The model is tested with real-time e-learning web site links and found that the e-contents in the web sites are recommended to the user based on its difficult levels as beginner level, intermediate level, and advanced level.
Creator
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Thomas, Benny.
Source
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Author's Submission
Publisher
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Christ(Deemed to be University)
Date
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2020-01-01
Contributor
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J, Chandra
Rights
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Open Access
Format
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PDF
Language
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English
Type
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PhD
Identifier
An unambiguous reference to the resource within a given context
<a href="http://hdl.handle.net/10603/364248" target="_blank" rel="noreferrer noopener">http://hdl.handle.net/10603/364248</a>
-
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/.
Title
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Articles
Article
Faculty Publications -Articles
Dublin Core
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Title
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Integrated IoT-Based Secure and Efficient Key Management Framework Using Hashgraphs for Autonomous Vehicles to Ensure Road Safety
Subject
The topic of the resource
Autonomous vehicles; Batch rekeying; Blockchain; Ethereum; Hashgraph; Logical key hierarchy
Description
An account of the resource
Autonomous vehicles offer various advantages to both vehicle owners and automobile companies. However, despite the advantages, there are various risks associated with these vehicles. These vehicles interact with each other by forming a vehicular network, also known as VANET, in a centralized manner. This centralized network is vulnerable to cyber-attacks which can cause data loss, resulting in road accidents. Thus, to prevent the vehicular network from being attacked and to prevent the privacy of the data, key management is used. However, key management alone over a centralized network is not effective in ensuring data integrity in a vehicular network. To resolve this issue, various studies have introduced a blockchain-based approach and enabled key management over a decentralized network. This technique is also found effective in ensuring the privacy of all the stakeholders involved in a vehicular network. Furthermore, a blockchain-based key management system can also help in storing a large amount of data over a distributed network, which can encourage a faster exchange of information between vehicles in a network. However, there are certain limitations of blockchain technology that may affect the efficient working of autonomous vehicles. Most of the existing blockchain-based systems are implemented over Ethereum or Bitcoin. The transaction-processing capability of these blockchains is in the range of 5 to 20 transactions per second, whereas hashgraphs are capable of processing thousands of transactions per second as the data are processed exponentially. Furthermore, a hashgraph prevents the user from altering the order of the transactions being processed, and they do not need high computational powers to operate, which may help in reducing the overall cost of the system. Due to the advantages offered by a hashgraph, an advanced key management framework based on a hashgraph for secure communication between the vehicles is suggested in this paper. The framework is developed using the concept of Leaving of Vehicles based on a Logical Key Hierarchy (LKH) and Batch Rekeying. The system is tested and compared with other closely related systems on the basis of the transaction compilation time and change in traffic rates. 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Creator
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Jha S.; Jha N.; Prashar D.; Ahmad S.; Alouffi B.; Alharbi A.
Source
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Sensors, Vol-22, No. 7
Publisher
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MDPI
Date
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2022-01-01
Identifier
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<a href="https://doi.org/10.3390/s22072529" target="_blank" rel="noreferrer noopener">https://doi.org/10.3390/s22072529</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126742616&doi=10.3390%2Fs22072529&partnerID=40&md5=c726c16e75b7139d1b2fadbd080e19a5" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126742616&doi=10.3390%2fs22072529&partnerID=40&md5=c726c16e75b7139d1b2fadbd080e19a5</a>
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All Open Access; Gold Open Access; Green Open Access
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A related resource
ISSN: 14248220; PubMed ID: 35408144
Format
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Online
Language
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English
Type
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Article
Coverage
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Jha S., School of Sciences, Christ (Deemed to be University), NCR-New Delhi Campus, Ghaziabad, 201003, India; Jha N., Department of Networking and Security, School of Computer Science and Engineering (SCSE), Lovely Professional University, Phagwara, 144411, India; Prashar D., Department of Networking and Security, School of Computer Science and Engineering (SCSE), Lovely Professional University, Phagwara, 144411, India; Ahmad S., Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia; Alouffi B., Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia; Alharbi A., Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
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Title
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Book Chapter
Book Chapter
Faculty Publications- Book Chapter
Dublin Core
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Title
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Integrated photonic devices for cancer detection
Description
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[No abstract available]
Creator
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Ibrar Jahan M.A.; Balaji V.R.; Sreevani N.R.G.; Sasikala D.; Jesuwanth Sugesh R.G.; Jenila C.; Hegde G.
Source
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Advances in All-optical Communication, pp. 10-.
Publisher
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Institute of Physics Publishing
Date
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2024-01-01
Identifier
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<a href="https://doi.org/10.1088/978-0-7503-5623-7ch10" target="_blank" rel="noreferrer noopener">https://doi.org/10.1088/978-0-7503-5623-7ch10</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211605627&doi=10.1088%2F978-0-7503-5623-7ch10&partnerID=40&md5=fd0192ef2c2707b0ef2ba205f7a61a82" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211605627&doi=10.1088%2f978-0-7503-5623-7ch10&partnerID=40&md5=fd0192ef2c2707b0ef2ba205f7a61a82</a>
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Restricted Access
Relation
A related resource
ISBN: 978-075035625-1; 978-075035621-3
Format
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Online
Language
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English
Type
The nature or genre of the resource
Book chapter
Coverage
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Ibrar Jahan M.A., RNS Institute of Technology, Visvesvaraya Technological University, Bengaluru, India; Balaji V.R., School of Electronics Engineering, Vellore Institute of Technology, Chennai Campus, Tamil Nadu, 600127, India; Sreevani N.R.G., Department of Computer Science and Engineering, S A Engineering College, Chennai, 600077, India; Sasikala D., Department of Electronics and Communication Engineering, Mookambigai College of Engineering, Pudukkottai, Tamil Nadu, 622502, India; Jesuwanth Sugesh R.G., Department of Electronics and Communication Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Jenila C., Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Virudhunagar, Tamil Nadu, India; Hegde G.
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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/.
Title
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Articles
Article
Faculty Publications -Articles
Dublin Core
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Title
A name given to the resource
Integrated skills for parenting the adolescents (ISPA): An intervention to strengthen parent- adolescent relationship /
Subject
The topic of the resource
Parenting
Description
An account of the resource
Review of Neuropsiquiatrica, Vol.76, Issue 4, pp.413-422, ISSN No: 1609-7394.
Creator
An entity primarily responsible for making the resource
Bino Thomas, Radhakrishnan Parthasarathy and Dinesh Bhugra
Source
A related resource from which the described resource is derived
<a href="https://search.bvsalud.org/gim/resource/en/lil-721973" target="_blank" title="Integrated skills for parenting the adolescents (ISPA): An intervention to strengthen parent- adolescent relationship" rel="noreferrer noopener">https://search.bvsalud.org/gim/resource/en/lil-721973</a>
Publisher
An entity responsible for making the resource available
World Health Oraganization
Date
A point or period of time associated with an event in the lifecycle of the resource
2014-01-06
Language
A language of the resource
English
Type
The nature or genre of the resource
Journal Article
-
https://archives.christuniversity.in/files/original/fcbf94d400f3f005d1c67fc3f4073919.png
ae8597b1fb62da5294ca14f6fc0ece85
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/.
Title
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Patents
Subject
The topic of the resource
Patent
Description
An account of the resource
Patent
Patent
Patents
Dublin Core
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Title
A name given to the resource
Integrated Synchronous buck converter emulation and method of design thereof /
Subject
The topic of the resource
Communication
Description
An account of the resource
Patent Number: 201941035816, Applicant: Jayanta Biswas.
The present invention is related to the Buck converter output ripple voltage analysis field, more particularly, the present invention is related to integrated synchronous buck converter emulation and method of design thereof. A DC-DC synchronous buck converter is emulated on FPGA based on geometric model of average inductor current. The integrated synchronous buck converter achieves precise regulation of the output voltage to fulfill the requirements of DVS applications.
Creator
An entity primarily responsible for making the resource
Samanta, Debabrata.
Source
A related resource from which the described resource is derived
<a href="https://drive.google.com/file/d/1S6nXHmOIpoU011gd4Aa1L2-Z-N7YNnKi/view" target="_blank" title="Integrated Synchronous buck converter emulation and method of design thereof /" rel="noreferrer noopener">https://drive.google.com/file/d/1S6nXHmOIpoU011gd4Aa1L2-Z-N7YNnKi/view</a>
Publisher
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Intellectual Property India
Date
A point or period of time associated with an event in the lifecycle of the resource
2019-03-14
Language
A language of the resource
English
Type
The nature or genre of the resource
Patent
-
https://archives.christuniversity.in/files/original/c4e49659606ca94af36993533b2ffe31.png
ae8597b1fb62da5294ca14f6fc0ece85
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/.
Title
A name given to the resource
Patents
Subject
The topic of the resource
Patent
Description
An account of the resource
Patent
Patent
Patents
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/.
Title
A name given to the resource
Integrated synchronous buck converter emulation and method of design thereof /
Subject
The topic of the resource
Communication
Description
An account of the resource
Patent Number: 201941035816, Applicant: Jayanta Biswas.
The present invention is related to the Buck converter output ripple voltage analysis field, more particularly, the present invention is related to integrated synchronous buck converter emulation and method of design thereof. A DC-DC synchronous buck converter is emulated on FPGA based on geometric model of average inductor current.
Creator
An entity primarily responsible for making the resource
Biswas, Jayanta.
Source
A related resource from which the described resource is derived
<a href="https://drive.google.com/file/d/1S6nXHmOIpoU011gd4Aa1L2-Z-N7YNnKi/view" target="_blank" title="Integrated synchronous buck converter emulation and method of design thereof /" rel="noreferrer noopener">https://drive.google.com/file/d/1S6nXHmOIpoU011gd4Aa1L2-Z-N7YNnKi/view</a>
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Intellectual Property India
Date
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2019-03-15
Language
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English
Type
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Patent
-
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Title
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Faculty Publications
Book Chapter
Faculty Publications- Book Chapter
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Shaikh, Akash; Toppo, Varsha; Al-Khayri, Jameel M.; Nagella, Praveen
Title
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Integrating Advanced Metabolomics with Plant Functional Genomics
Date
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01-01-2025
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High-Throughput Plant Metabolomics;pp.335-349
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<a href="https://doi.org/10.1079/9781800626911.0017" target="_blank" rel="noreferrer noopener">https://doi.org/10.1079/9781800626911.0017</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105017104636?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105017104636?origin=resultslist</a>
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Shaikh A., Department of Life Sciences, School of Sciences, CHRIST (Deemed to be University), Hosur Road, Bengaluru, India; Toppo V., Department of Life Sciences, School of Sciences, CHRIST (Deemed to be University), Hosur Road, Bengaluru, India; Al-Khayri J.M., Department of Agricultural Biotechnology, College of Agriculture and Food Sciences, King Faisal University, Al-Ahsa, Saudi Arabia; Nagella P., Department of Life Sciences, School of Sciences, CHRIST (Deemed to be University), Hosur Road, Bengaluru, India
Description
An account of the resource
Metabolomics encompasses the entire suite of small-molecule compounds or metabolites synthesized by an or ganism, whereas functional genomics refers to the gene-level functioning of an organism. The genome of a plant will dictate its metabolome, but the link between the two omics data may not always be clearly visible or properly explored. This chapter delves into the integration of advanced metabolomics with plant functional genomics, highlighting its pivotal role in advancing our understanding of plant biology and its applications in agriculture. Metabolomics provides a comprehensive analysis of small molecules, bridging the gap between genotype and phenotype by elucidating the dynamic interactions within plant systems. Key techniques such as mass spectrom etry and nuclear magnetic resonance are explored, emphasizing their importance in high-throughput and high-resolution metabolite profiling. The chapter further discusses the synergy between metabolomics and other omics technologies, including genomics, transcriptomics, and proteomics, underscoring its significance in iden tifying gene functions and metabolic pathways linked to complex traits such as stress tolerance. Applications in plant breeding are also highlighted, showcasing how metabolomics can drive the development of crops with en hanced stress resilience, yield, and nutritional quality. The chapter concludes by emphasizing the transformative potential of this integrated approach in shaping future agricultural practices and improving food security. CAB International 2025. All rights reserved.
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Functional genomics; Gene function; Metabolomics; Omics integration; Plant breeding
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CABI International
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ISBN: 978-180062690-4; 978-180062689-8;
Language
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English
Type
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Book chapter
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Title
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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Title
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Integrating AI and Cybersecurity: Advancing Autonomous Vehicle Security and Response Mechanisms
Subject
The topic of the resource
AI integration; Dynamic response mechanisms; Risk mitigation; Vehicle security operation centers
Description
An account of the resource
The rapid evolution of autonomous and connected vehicles has led to their integration with numerous technologies and software, rendering them vulnerable targets for cybersecurity attacks. While efforts have traditionally focused on preventing these attacks, the escalating risk underscores the importance of also vindicating their wallop. Nevertheless, this procedure is often onerous & facade scalability confronted, particularly due to connectivity issues in automobiles. This research advises a vehicle-based vibrant imposition response scheme, enabling swift responses to a variety of incidents and reducing reliance on external security centers. The classification encompasses an inclusive range of probable retorts, a procedure for evaluating retorts, & innumerable assortment approaches. Implemented on an embedded platform, the solution was evaluated using two distinct cyberattack use cases, highlighting its adaptability, responsiveness, volume for dynamic arrangement constraint alterations & nominal memory trail. Concurrently, this paper presents an innovative (AVSF) that synergistically integrates (AI) and cybersecurity techniques to fortify AV resilience against evolving threats. Additionally, the framework incorporates advanced cybersecurity measures such as encryption, authentication, and intrusion detection to mitigate vulnerabilities and safeguard critical AV systems. The fusion of AI and cybersecurity not only enhances AV security posture but also enables intelligent cyber threat monitoring and response capabilities. Extensive simulations and experimental evaluations demonstrate the efficacy of the AVSF in real-time scenarios, contributing to the development of robust security solutions for autonomous vehicle deployment and advancing safer transportation systems in the era of AI-driven mobility. 2024 IEEE.
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Namburi V.L.; Adapa S.R.; Chamala S.S.K.; Yerram M.; Gupta P.; Upreti K.
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2024 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024 - Proceedings, pp. 253-258.
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Institute of Electrical and Electronics Engineers Inc.
Date
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2024-01-01
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<a href="https://doi.org/10.1109/ETNCC63262.2024.10767464" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ETNCC63262.2024.10767464</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214649304&doi=10.1109%2FETNCC63262.2024.10767464&partnerID=40&md5=aad695f88ab578c41a11cb076c0cb861" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214649304&doi=10.1109%2fETNCC63262.2024.10767464&partnerID=40&md5=aad695f88ab578c41a11cb076c0cb861</a>
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ISBN: 979-835035326-6
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Online
Language
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English
Type
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Conference paper
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Namburi V.L., Software Systems Engineer Danlaw Inc, MI, United States; Adapa S.R., Software Engineer OpenLogix LLC, MI, United States; Chamala S.S.K., Software Developer Software Bots Inc, MI, United States; Yerram M., Computer Science with IOT VNRVJIET, Hyderabad, India; Gupta P., Computer Science Engineering ADGIPS, Delhi, India; Upreti K., Dept. of Computer Science, Christ University, Delhi NCR Campus, India
-
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Title
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Faculty Publications
Book Chapter
Faculty Publications- Book Chapter
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Hussain, Shayak
Title
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Integrating AI into Corporate Social Responsibility (CSR) for Ethical and Sustainable Business Practices
Date
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01-01-2025
Source
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Integrating Sustainability Principles With AI-Driven Entrepreneurial Strategies;pp.41-70
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<a href="https://doi.org/10.4018/979-8-3373-1107-4.ch003" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-1107-4.ch003</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105021453284?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105021453284?origin=resultslist</a>
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Hussain S., Christ University, Ghaziabad, India
Description
An account of the resource
The rapid advancement of artificial intelligence (AI) technologies has significantly transformed various facets of business operations, including corporate social responsibility (CSR). As businesses strive to align their growth strategies with ethical, social, and environmental responsibilities, AI emerges as a powerful tool to enhance the effectiveness of CSR initiatives. This research investigates the integration of AI into CSR, exploring its potential to drive more sustainable business practices, improve transparency, and foster ethical decision-making within organizations. By employing a combination of qualitative and quantitative research methods, this study examines how AI-powered analytics, automation, and decision-making frameworks can optimize CSR efforts. Key areas of exploration include AI's role in enhancing supply chain sustainability, optimizing resource allocation, detecting unethical business practices, and enabling real-time monitoring and reporting of CSR initiatives. 2026, IGI Global Scientific Publishing.
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IGI Global
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ISBN: 979-833731109-8; 979-833731107-4;
Language
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English
Type
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Book chapter
Rights
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Restricted Access; Hardcopy may be available in the library
-
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Title
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
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Title
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Integrating AI Tools into HRM to Promote Green HRM Practices
Subject
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AI; Green HRM; HRM; Sustainability
Description
An account of the resource
The image of Human Resource Management (HRM) is undergoing a drastic transformation. The conventional methods are evolving due to the emergence of technology, especially with the integration of Artificial Intelligence (AI) and data analytics into the HR processes. With the rapidly changing concept of the overall growth of an organization, AI is becoming a vital stimulant for sustainable growth. AI-powered tools promote data-driven decision-making for talent acquisition, performance management, workforce training and development, optimization of energy consumption and waste reduction. Green HRM aligns these efforts by integrating sustainability considerations into talent management strategies, nurturing employees eco-engagement, and promoting environmentally responsible practices within the workforce. This research paper aims to explore the synergies between AI tools and Green HRM practices, investigating how the integration of AI technologies into HR processes can contribute to the promotion of environmental sustainability. By examining real-world case studies, this study aims to investigate the potential of AI-powered solutions in shaping the future of HRM through the lens of sustainability. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Creator
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John J.E.; Pramila S.
Source
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Lecture Notes in Networks and Systems, Vol-878, pp. 249-259.
Publisher
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Springer Science and Business Media Deutschland GmbH
Date
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2024-01-01
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<a href="https://doi.org/10.1007/978-981-99-9489-2_22" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-9489-2_22</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189563916&doi=10.1007%2F978-981-99-9489-2_22&partnerID=40&md5=4395bfc01da2b0b6604fb46b36f2cb5b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189563916&doi=10.1007%2f978-981-99-9489-2_22&partnerID=40&md5=4395bfc01da2b0b6604fb46b36f2cb5b</a>
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Restricted Access
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ISSN: 23673370; ISBN: 978-981999488-5
Format
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Online
Language
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English
Type
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Conference paper
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
John J.E., Christ (CHRIST (Deemed to be University), Delhi NCR, India; Pramila S., Christ (CHRIST (Deemed to be University), Delhi NCR, India
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Dublin Core
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Title
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Faculty Publications
Book Chapter
Faculty Publications- Book Chapter
Dublin Core
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Guzm, Maria Carolina Ordenes; Chacaltana, Camila Martinez; Olguin, Aida Leyton; Prakasha, G.S.
Title
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Integrating Artificial Intelligence in Education: Insights From a Teacher Training Workshop
Date
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01-01-2025
Source
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Foundations and Frameworks for AI in Education;pp.371-406
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<a href="https://doi.org/10.4018/979-8-3373-2397-8.ch013" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-2397-8.ch013</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015930816?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015930816?origin=resultslist</a>
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Guzm M.C.O., Pontificia Universidad Catica de Chile, Chile; Chacaltana C.M., Academia de Biotecnologia Agricola, Chile; Olguin A.L., Academia de Biotecnologia Agricola, Chile; Prakasha G.S., Christ University, India
Description
An account of the resource
This chapter explores the impact of an in-person AI training workshop on Chilean in-service teachers across a network of four schools. Through a mixed-methods approach-including pre-and post-surveys with qualitative and quantitative data-the study examines shifts in teacher attitudes, knowledge, and intentions to use AI in educational practice. Results show increased confidence, pedagogical alignment, and ethical awareness, particularly regarding inclusion and differentiated instruction. The chapter also highlights the importance of contextualized training, gender representation, and long-term support to ensure equitable and meaningful AI integration in Latin American classrooms. 2026 by IGI Global Scientific Publishing. All rights reserved.
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IGI Global
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ISBN: 979-833732399-2; 979-833732397-8;
Language
A language of the resource
English
Type
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Book chapter
Rights
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Restricted Access; Hardcopy may be available in the library
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Title
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Faculty Publications
Book Chapter
Faculty Publications- Book Chapter
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Kismawadi, Early Ridho; Irfan, Mohammad; Harahap, Isnaini
Title
A name given to the resource
Integrating artificial intelligence in Islamic financial management: Opportunities and challenges in maintaining Shariah compliance
Date
A point or period of time associated with an event in the lifecycle of the resource
01-01-2025
Source
A related resource from which the described resource is derived
Indigenous Empowerment through Human-Machine Interactions: The Challenges and Strategies from Business Lenses;pp.273-288
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<a href="https://doi.org/10.1108/978-1-83608-068-820251016" target="_blank" rel="noreferrer noopener">https://doi.org/10.1108/978-1-83608-068-820251016</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105006477790?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105006477790?origin=resultslist</a>
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Kismawadi E.R., IAIN Langsa, Indonesia; Irfan M., Christ University, India; Harahap I., Universitas Islam Negeri Sumatera Utara, Indonesia
Description
An account of the resource
The objective of this study is to examine the potential incorporation of Artificial Intelligence (AI) technology into financial management practices that are based on Islamic principles, with a particular emphasis on ensuring Shariah compliance. The literature analysis methodological approach is employed to identify the opportunities and challenges associated with the adoption of AI in the Islamic Finance (IF) environment. The results indicate that the implementation of AI can enhance the efficacy, transparency, and precision of IF operations. However, there are numerous challenges associated with Shariah compliance and ethics. The findings of this study emphasize the importance of establishing a regulatory framework that is consistent with Shariah principles in order to ensure the successful implementation of AI in Islamic financial institutions (IFI). The need for collaboration between finance experts and academics to ensure that the technology is implemented in accordance with Shariah principles, as well as the expansion of training for IF (Islamic Finance) practitioners regarding the implications of AI, are among the recommendations. Future research should examine the influence of more specific AI implementation strategies on Islamic conformance and operational efficacy in the context of IF. 2025 Early Ridho Kismawadi, Mohammad Irfan and Isnaini Harahap. All rights reserved.
Subject
The topic of the resource
Artificial Intelligence; Big data; Financial management; IF; Natural language processing; Technology integration
Publisher
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Emerald Publishing
Relation
A related resource
ISBN: 978-183608068-8; 978-183608069-5;
Language
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English
Type
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Book chapter
Rights
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Restricted Access; Hardcopy may be available in the library
-
Dublin Core
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Title
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Faculty Publications
Book Chapter
Faculty Publications- Book Chapter
Dublin Core
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Balaji, K.
Title
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Integrating Artificial Intelligence in the Blue Economy: A Case-Based Study of Smart Marine Ecosystems for Climate Change Mitigation and Circular Economy
Date
A point or period of time associated with an event in the lifecycle of the resource
01-01-2026
Source
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AI and Climate-Smart Policies for Blue Economy: Eco-Digital Futures;pp.153-179
Identifier
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<a href="https://doi.org/10.4018/979-8-3373-5238-1.ch007" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-5238-1.ch007</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033898871?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033898871?origin=resultslist</a>
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Balaji K., Christ University, Bengaluru, India
Description
An account of the resource
The Blue Economy represents a critical pathway toward sustainable development, balancing economic growth with the conservation of marine ecosystems. This chapter explores the transformative role of Artificial Intelligence (AI) in advancing sustainable practices within the Blue Economy through a series of global case studies. It examines how leading companies and initiativessuch as Sinay, Saildrone, Sea.AI, Buffalo Automation, IBM, Microsoft, Ocean Cleanup, and Veolia Groupleverage AI-driven solutions for real-time environmental monitoring, predictive climate modeling, autonomous navigation, and circular waste management. Key insights reveal that AI enables data-driven decision-making, reduces carbon emissions, enhances operational efficiency, and supports closed-loop recycling systems, while confronting challenges related to data integration, regulatory frameworks, and economic viability. Policy implications highlight the urgent need for adaptive regulations, open data ecosystems, and public-private partnerships to scale smart marine ecosystems globally. 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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A related resource
ISBN: 979-833735240-4; 979-833735238-1; 979-833735239-8;
Language
A language of the resource
English
Type
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Book chapter
Rights
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Restricted Access; Hardcopy may be available in the library
-
Dublin Core
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Title
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Faculty Publications
Conference Paper
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Iwendi, Celestine; Nwibo, Ezekiel Gabriel; Sharma, Vandana; Lemeke, Collins; Uwah, Salome Enoshi; Ukura, Kumasuun Kimberly
Title
A name given to the resource
Integrating Behavioural Science using the Psycho-Intelligence Framework in Connected Systems
Date
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01-01-2025
Source
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2025 IEEE 5th International Conference on ICT in Business Industry and Government, ICTBIG 2025;
Identifier
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<a href="https://doi.org/10.1109/ICTBIG68706.2025.11323725" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG68706.2025.11323725</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032910899?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032910899?origin=resultslist</a>
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Iwendi C., Centre of Intelligence of Things (CIoTh), University of Greater Manchester, Bolton, United Kingdom; Nwibo E.G., Centre of Intelligence of Things (CIoTh), University of Greater Manchester, Bolton, United Kingdom; Sharma V., Christ University, Computer Science Department, Bengaluru, India; Lemeke C., Centre of Intelligence of Things (CIoTh), University of Greater Manchester, Bolton, United Kingdom; Uwah S.E., Centre of Intelligence of Things (CIoTh), University of Greater Manchester, Bolton, United Kingdom; Ukura K.K., Centre of Intelligence of Things (CIoTh), University of Greater Manchester, Bolton, United Kingdom
Description
An account of the resource
The fast-growing convergence of neuroscience, behaviour computing, and adaptive artificial intelligence (AI) offers the possibility to transform human, machine interaction. This work presents Psycho-Intelligence, a new, closed-loop system that merges electroencephalography (EEG) and inertial motion unit (IMU) signals to adaptively recognise and react to users' cognitive and affective states. Levying low-cost wearable sensors (Muse EEG and MPU-6050), the system has real-time signal acquisition, sophisticated preprocessing, spectral and statistical feature extraction, as well as multimodal fusion features. Dimensionality reduction and feature selection techniques, including Principal Component Analysis and XGBoost gain metrics, enhance learning optimally. Multiple machine learning algorithms like Random Forest, SVM and XGBoost are trained to identify engagement states with high accuracy, warranted by extensive testing through cross-validation, ROC AUC, and F1-scores. The pipeline is incorporated into an adaptive feedback system that can regulate chatbot tone, learning material, or interactive graphics based on detected user states. Statistical validation with linear mixed models confirms the robustness of EEG-derived measurements in engagement prediction. The research establishes a new paradigm for emotionally intelligent AI systems and provides a technical foundation for ethical, real-time psycho-behavioural intelligence for communication networks, education systems, and cognitive health monitoring. 2025 IEEE.
Subject
The topic of the resource
Adaptive Human-AI Interaction; Behavioural Computing; EEG-IMU Fusion; Multimodal Emotion Recognition; Psycho-Intelligence
Publisher
An entity responsible for making the resource available
Institute of Electrical and Electronics Engineers Inc.
Relation
A related resource
ISBN: 979-833157981-4;
Language
A language of the resource
English
Type
The nature or genre of the resource
Conference paper
Rights
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Restricted Access; Hardcopy may be available in the library
-
Dublin Core
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Title
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
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Dabbir, Rupesh; Logeshwaran, J.
Title
A name given to the resource
Integrating Big Data Management with Machine Learning in Cloud Environments
Date
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01-01-2025
Source
A related resource from which the described resource is derived
2025 International Conference on Networks and Cryptology, NETCRYPT 2025;pp.443-447
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<a href="https://doi.org/10.1109/NETCRYPT65877.2025.11102667" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/NETCRYPT65877.2025.11102667</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015469770?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015469770?origin=resultslist</a>
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Dabbir R., Google LLC, San Jose, CA, United States; Logeshwaran J., Christ University, Department of Computer Science, Karnataka, Bengaluru, 560029, India
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In past years, the consideration of cloud environments for big data management and machine learning techniques has increased exponentially. However, the massive amount of data that is made by companies and Internet of Things (IoT) devices has presented the industry with a challenge in storage. In addition, conventional data management methods are unable to manage the complexity, diversity, and volume of this big data. Consequently, cloud environments integrate big data management and machine learning techniques. Combining big data management through cloud environments equipped with machine learning has multiple benefits. First, it is an efficient way to process and analyze large datasets in a distributed and scalable manner. Second, it helps businesses to provide insights and make time to make datadriven decisions. Thirdly, it enables the system to learn from data on an ongoing basis, which helps improve the quality and accuracy of the data. Now, this integration also enhances the overall performance of cloud systems as data management tasks are automated, minimizing manual efforts. Utilizing Big Data Management and Machine Learning Techniques in Cloud Environment to Address the Drawbacks of Conventional Data Management Approaches and Exploit the Value of Big Data These two technologies, when integrated, will allow the enterprise to manage significant amounts of data and derive important insights to arrive at better decisions. Cloud environments provide the automation and scalability required to push this integration further and change considerable data use in businesses. 2025 IEEE.
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Automation; Cloud Systems; Complexity; Conventional Data; Diversity; Internet Of Things
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833152605-4;
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English
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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
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Faculty Publications -Articles
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Kumar, Senthil; Ramprasath, J.; Kalpana, V.; Rajagopal, Manikandan; S, Maheswaran; Gupta, Rupesh
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Integrating brain-inspired computation with big-data analytics for advanced diagnostics in neuroradiology
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01-01-2025
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Neuroscience Informatics;Volume;5;Issue;2;Article No.;100202;
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<a href="https://doi.org/10.1016/j.neuri.2025.100202" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.neuri.2025.100202</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105004811471?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105004811471?origin=resultslist</a>
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Kumar S., Department of Information Technology, Karpagam Academy of Higher Education, Deemed to be University, Eachanari, Coimbatore, ?641021, India; Ramprasath J., Department of Information Technology, Dr. Mahalingam College of Engineering and Technology, Pollachi, India; Kalpana V., Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India; Rajagopal M., School of Business and Management, Christ university, Bangalore, India; S M., Department of Electronics and Communication Engineering, Kongu Engineering College, Erode, India; Gupta R., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
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Introduction: Neuroradiology encounters considerable difficulties owing to imaging data's intricacy and high-dimensional characteristics. Conventional diagnostic techniques often encounter challenges regarding precision and scalability, resulting in delays and possible misinterpretations. This paper presents the Big-Data Analytics-based Diagnostics (BDA-D) framework, a revolutionary method using computational models derived from neural architectures and sophisticated analytics to tackle these difficulties. Methods: The BDA-D architecture utilizes data mining, pattern recognition, and machine learning to glean useful neuroanatomical characteristics from massive datasets. By simulating human thought processes, this method speeds up clinical decision-making and improves diagnostic accuracy. To evaluate the effectiveness of the framework, it is put to the test in a clinical environment. Results and Discussion: Diagnostic precision, processing speed, and dependability are all enhanced by experimental validation. By detecting even the most minute neuroanatomical changes, BDA-D allows for more accurate diagnosis than traditional approaches. Based on the results, neuroradiologists may improve their practices by using cutting-edge computational methods to close the gap between data-driven analytics and their practical use in the clinic. BDA-D discovers important patterns from high-dimensional neuroimaging data through biologically inspired neural networks, reaching a remarkable diagnosis accuracy of 97.18%. Its 95.42% increase in processing speed allows rapid study of important disorders such as strokes and neurodegenerative diseases. BDA-D reduces inter-observer variability with a dependable value of 94.96%, increasing clinical confidence in AI-assisted diagnosis. Conclusion: A revolutionary change in neurodiagnostics, the BDA-D framework improves efficiency and reliability. This method has the potential to completely transform neuroradiology by combining big-data analytics with sophisticated computer models. It will allow for more rapid and precise diagnosis. 2025 The Authors
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Advanced imaging; Big-data analytics; Brain-inspired computation; Diagnostic framework; Machine learning; Neuroradiology
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Elsevier Masson s.r.l.
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ISSN: 27725286;
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English
Type
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Article
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All Open Access; Gold Open Access; Green Open Access
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online