638
20
16481
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Title
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Savadatti, Mamta B; Kushalatha, M.R.; Vathsala, M.K.; Birajdar, Shantarani S; Hamsalekha, R.; Santhrupth, C.S.; Bale, Ajay Sudhir; Gayatri, Mantripragada Ramaa
Title
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An Integrated Approach to Green Cloud Solutions for Energy-Efficient Sustainable IT and Carbon Footprint Reduction
Date
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01-01-2025
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2025 2nd Asia Pacific Conference on Innovation in Technology, APCIT 2025;
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<a href="https://doi.org/10.1109/APCIT65661.2025.11411210" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/APCIT65661.2025.11411210</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035618460?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035618460?origin=resultslist</a>
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Savadatti M.B., New Horizon College of Engineering, Department of Electronics and Communication Engineering, Bengaluru, India; Kushalatha M.R., Nitte (Deemed to Be University), Nitte Meenakshi Institute of Technology, Department of Ece, Bengaluru, India; Vathsala M.K., Cambridge Institute of Technology, Information Science and Engineering, Bengaluru, India; Birajdar S.S., Vijaya Vittala Institute of Technology, Department of Artificial Intelligence and Machine Learning, Bengaluru, India; Hamsalekha R., New Horizon College of Engineering, Department of Electronics and Communication Engineering, Bengaluru, India; Santhrupth C.S., Christ University, Department of Aiml & Ds, Bengaluru, India; Bale A.S., New Horizon College of Engineering, Department of Electronics and Communication Engineering, Bengaluru, India; Gayatri M.R., New Horizon College of Engineering, Department of Electronics and Communication Engineering, Bengaluru, India
Description
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Cloud computing has become a very important part in everyday life, but this has also made a lot of carbon footprint because of the energy consumption in the data centers. The pandemic had affected these emissions, and they quickly came back, which has shown the requirement for sustainable solutions which will help in fighting the increase in carbon footprint. For these problems, the green computing technology will give probable solutions by promoting the technology that would be responsible enough to decrease these effects of harming environment. It will have techniques like smarter system designs, operations that are energy efficient, and smart techniques for optimization. This study explores how the above set principles can reduce the overall digital carbon footprint and help to create economically viable businesses. This approach provides a forward path for technology progress and profitability aligning with the environment sustainability which is a necessary component for business longevity. 2025 IEEE.
Subject
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Carbon Footprint Reduction; Digital Sustainability; Energy-Efficient Data Centers; Environmental Computing; Green Cloud Computing; Sustainable IT Infrastructure
Publisher
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833152306-0;
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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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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Workplace bullying in the service sector
Subject
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Adult bullying; work harassment scale; workplace bullying
Description
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Context: Bullying is a problem that people, the world over, grapple with. It is manifest in different forms among different sections of people. Despite its prevalence, workplace bullying has not received much attention in scholarly literature in India. It is also not widely acknowledged as a threat to individual and organizational well-being. The purpose of this study is to add to the existing body of literature on the topic and to draw attention to the gravity of the issue. Aims: The primary objectives are to identify if there exist variations in its incidence on the basis of gender and years of experience, to identify the source of negative behavior, and the type of bullying that is most prevalent. Settings and Design: The study is a type of cross-sectional, descriptive study. Subjects and Methods: Data have been collected from a sample of 84 respondents using the Work Harassment Scale. All respondents are white-collar employees of the service sector in the cities of India. The data were analyzed using IBM SPSS v25. Results: The results find that there is no difference in the incidence of bullying on the basis of either gender or years of experience. Moreover, the source of negative behavior is generally one's superiors, and the most prevalent type is 'verbal aggression.' Conclusions: The study concludes with suggestions of steps to be implemented at the national and organizational level, to combat the problem. 2022 Authors. All rights reserved.
Creator
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Savapandit N.; Bharathi
Source
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Indian Journal of Social Psychiatry, Vol-38, No. 2, pp. 143-147.
Publisher
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Wolters Kluwer Medknow Publications
Date
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2022-01-01
Identifier
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<a href="https://doi.org/10.4103/ijsp.ijsp_176_20" target="_blank" rel="noreferrer noopener">https://doi.org/10.4103/ijsp.ijsp_176_20</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150907777&doi=10.4103%2Fijsp.ijsp_176_20&partnerID=40&md5=57727d0dd835b8aef84b9131e0ef6370" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150907777&doi=10.4103%2fijsp.ijsp_176_20&partnerID=40&md5=57727d0dd835b8aef84b9131e0ef6370</a>
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All Open Access; Gold Open Access
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A related resource
ISSN: 9719962; CODEN: ISPSE
Format
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Online
Language
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English
Type
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Article
Coverage
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Savapandit N., Department of Sociology and Social Work, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Bharathi, Department of Sociology and Social Work, CHRIST (Deemed to be University), Karnataka, Bengaluru, India
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Title
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Faculty Publications
Article
Faculty Publications -Articles
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Savarimuthu, James Robert
Title
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The development and validation of the digital intelligence scale for students
Date
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01-01-2026
Source
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Education and Information Technologies;
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<a href="https://doi.org/10.1007/s10639-026-14003-2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10639-026-14003-2</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036186851?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036186851?origin=resultslist</a>
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Savarimuthu J.R., Department of Psychology, CHRIST University, Bangalore, 560074, India
Description
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Digital intelligence is increasingly recognized as a vital skill in navigating both academic and personal digital environments, yet existing tools often use multidimensional or adult-oriented frameworks. This study aims to develop and validate the Digital Intelligence Scale for Students (DISS), a unidimensional self-report instrument designed to assess the general digital intelligence of school, college, and university students. The study was conducted in two phases. In the first phase, data was collected from 786 students in India to examine the factor structure of the model. The analysis supported a unidimensional model, indicating that all items measured a single underlying construct. In the second phase, data was collected from 611 students in India to confirm the unidimensional model. Results supported a robust unidimensional structure, with excellent internal consistency (? = 0.954). The DISS was found to be significantly correlated with Internet Skills Scale and Digital Literacy Scale, providing evidence for convergent validity. Divergent validity was assessed using State-Trait Anxiety Inventory and Big Five Personality Inventory. This scale provides a practical framework for evaluating digital readiness in educational settings and guiding interventions. Subsequent studies could validate its relevance across cultural contexts and examine developmental trajectories in digital intelligence. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026.
Subject
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Digital competence; Digital intelligence; Digital literacy; Digital wellbeing; Psychometric validation; Scale development
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Springer
Relation
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ISSN: 13602357;
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
-
https://archives.christuniversity.in/files/original/f29398a7487e5de6d1739cf99145f2f6.pdf
d27c9664050cdbbd01f62694f729b526
Dublin Core
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Title
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MPHIL
Mphil
Mphil Thesis
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Title
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STUDY ON ORGANIZATIONAL CULTURE IN RELATION TO HUMAN RESOURCE MANAGEMENT PRACTICES IN HOTEL INDUSTRY IN BANGALORE
Creator
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Savio Nigli Kerwin
Date
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2012
Source
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Management Studies
Description
An account of the resource
This research focuses and examines the relationship between organizational culture and human resource management practices in hotel industry in Bangalore. Organizational culture is palpable in any organization which has a diversified work force. Hotel industry is built with employees with various values and beliefs. These factors reflect in the culture and are related with the various human resource management practices. There is a significant relationship between organizational culture and human resource management practices in the hotel industry in Bangalore.
There were eleven dimensions of organizational culture and thirteen dimensions of human resource management practices that were considered for examining the relationship between organizational culture and human resource management practices. The sample for the study was 135 mid-level managers of different category of hotels in the geographical location of Bangalore. Organizational culture survey scale developed by Pareek, U. (2003) and human resource management practices scale developed by Sebastian, S., & Patrick, H. A. (2010) was administered for this study.
This research concludes by the finding fact that there is significant relationship between organizational culture and human resource management practices in the hotel industry. The study also shows that the culture in hotel industry is narcissistic.
Keywords: Organizational culture, Human resource management practices, hotel industry
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Title
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Faculty Publications
Article
Faculty Publications -Articles
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Savio, Abel; Srivastava, Manshvi; Jose, Maria Rose; Kumar, Nikita; Elanjickal, Crissin Sojan; Singh, Kuljeet
Title
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A review of artificial intelligence enhanced cognitive behavioural therapy using the BECK AI BOT for mental health interventions
Date
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01-01-2026
Source
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Discover Mental Health;Volume;6;Issue;1;Article No.;43;
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<a href="https://doi.org/10.1007/s44192-026-00391-x" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s44192-026-00391-x</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034537728?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034537728?origin=resultslist</a>
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Savio A., Department of Psychology, School of Social Sciences, Christ University, Delhi-NCR, New Delhi, 201003, India; Srivastava M., Department of Psychology, School of Social Sciences, Christ University, Delhi-NCR, New Delhi, 201003, India; Jose M.R., Department of Psychology, School of Social Sciences, Christ University, Delhi-NCR, New Delhi, 201003, India; Kumar N., Department of Psychology, School of Social Sciences, Christ University, Delhi-NCR, New Delhi, 201003, India; Elanjickal C.S., School of Business and Management, Christ University, New Delhi, Delhi-NCR, 201003, India; Singh K., Department of Computer Science, School of Sciences, Christ University, New Delhi, Delhi-NCR, 201003, India
Description
An account of the resource
The integration of artificial intelligence (AI) and cognitive behaviour therapy (CBT) is a revolutionary solution to the global mental health issue, characterized by increasing need and decreased access to treatment. This research investigates the potential of AI-fortified cognitive behavioural therapy technologies, including chatbots, virtual reality, and adaptive learning modules, to enhance the efficacy, accessibility, and individualization of treatment for anxiety, depression, and PTSD. The study evaluates the scalability, ethical issues, and therapeutic efficacy of the therapies by combining peer-reviewed and experimental data. The suggested methodology combines AI-driven conversational therapy with predictive modelling to deliver individualized, real-time mental health treatment. In this study, a conceptual chatbot prototype, designated BECK-AI BOT, was developed to illustrate the applications interface and functionality, enhancing accessibility for both patients and therapists in the future. This study does not present new clinical trial data. All reported symptom-reduction and engagement findings are drawn from previously published studies of existing AI-driven CBT systems (e.g., Woebot, Wysa, Eleos, Limbic). The present work offers a narrative synthesis of current evidence and introduces a conceptual architecture and prototype (BECK-AI BOT), without evaluating it clinically. Notwithstanding these difficulties, problems persist, including a lack of long-term efficacy statistics, cultural sensitivity issues, and moral reservations about over-reliance on AI during emergencies. The argument comes in the form of AI possibly improving, not replacing, human therapists, emphasizing hybrid systems for fair treatment. Future research needs to advance emotional intelligence within AI, which combines AI-driven conversational therapy and predictive modelling to deliver real-time, personalized mental health services. The Author(s) 2026.
Subject
The topic of the resource
Artificial intelligence (AI); Cognitive behavioural therapy (CBT); Depression; Mental health; Post-Traumatic stress disorder (PTSD)
Publisher
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Springer Nature
Relation
A related resource
ISSN: 27314383;
Language
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English
Type
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Article
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All Open Access; Gold Open Access; Green Open Access
Format
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online
-
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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Intensity of hospital waste generation and disposal in the selected hospitals in Kerala, India: an analysis based on hospital ownership
Subject
The topic of the resource
Disposal; Hospital waste generation rate; Hospital waste management; Ownership-wise; Treatment
Description
An account of the resource
Management of hospital wastes has been considered as an integral part of hospital hygiene and infection control, which in turn depends on the intensity of waste generation and disposal. This study analyses the ownership-wise intensities of hospital waste generated, treated and disposed in the selected hospitals in the state of Kerala, India. These intensities are examined using secondary data collected from four districts of Kerala for the period from 2010 to 2014. The intensity of hospital waste generation is measured on the basis of per bed per kilogram per day and also per patient per kilogram per day basis. The study shows that private hospitals are producing significantly higher amount of waste than government and co-operative hospitals. However, private hospitals are found to be more efficient compared to government hospitals in treating and disposing the hospital waste. It is also found that the co-operative hospitals are well-organized in treating and disposing the liquid waste compared with other hospitals in Kerala. 2023, The Author(s), under exclusive licence to Springer Nature Japan KK, part of Springer Nature.
Creator
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Savitha K.L.; Joseph T.J.
Source
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Journal of Material Cycles and Waste Management, Vol-25, No. 3, pp. 1760-1770.
Publisher
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Springer
Date
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2023-01-01
Identifier
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<a href="https://doi.org/10.1007/s10163-023-01649-0" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10163-023-01649-0</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151476375&doi=10.1007%2Fs10163-023-01649-0&partnerID=40&md5=82086dff94482d8a428085f02e991aa1" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151476375&doi=10.1007%2fs10163-023-01649-0&partnerID=40&md5=82086dff94482d8a428085f02e991aa1</a>
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Restricted Access
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ISSN: 14384957
Format
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Online
Language
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English
Type
The nature or genre of the resource
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
Savitha K.L., Department of Economics, Christ University, Yeshwanthpur Campus, Bangalore, 560058, India; Joseph T.J., Department of Economics, Central University of Kerala, Kerala, Kasaragod, India
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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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CloudML: Privacy-Assured Healthcare Machine Learning Model for Cloud Network
Subject
The topic of the resource
Cloud privacy; Clustering; Encryption; Healthcare datasets; Machine learning; Network performance
Description
An account of the resource
Cloud computing is the need of the twenty-first century with an exponential increase in the volume of data. Compared to any other technologies, the cloud has seen fastest adoption in the industry. The popularity of cloud is closely linked to the benefits it offers which ranges from a group of stakeholders to huge number of entrepreneurs. This enables some prominent features such as elasticity, scalability, high availability, and accessibility. So, the increase in popularity of the cloud is linked to the influx of data that involves big data with some specialized techniques and tools. Many data analysis applications use clustering techniques incorporated with machine learning to derive useful information by grouping similar data, especially in healthcare and medical department for predicting symptoms of diseases. However, the security of healthcare data with a machine learning model for classifying patients information and genetic data is a major concern. So, to solve such problems, this paper proposes a Cloud-Machine Learning (CloudML) Model for encrypted heart disease datasets by employing a privacy preservation scheme in it. This model is designed in such a way that it does not vary in accuracy while clustering the datasets. The performance analysis of the model shows that the proposed approach yields significant results in terms of Communication Overhead, Storage Overhead, Runtime, Scalability, and Encryption Cost. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Creator
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Savitha S.; Ravichandran S.K.
Source
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Lecture Notes in Networks and Systems, Vol-213, pp. 51-64.
Publisher
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Springer Science and Business Media Deutschland GmbH
Date
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2022-01-01
Identifier
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<a href="https://doi.org/10.1007/978-981-16-2422-3_5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-2422-3_5</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115172237&doi=10.1007%2F978-981-16-2422-3_5&partnerID=40&md5=df2db7df7e45b9f22f7a937c73565bf3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115172237&doi=10.1007%2f978-981-16-2422-3_5&partnerID=40&md5=df2db7df7e45b9f22f7a937c73565bf3</a>
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Restricted Access
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ISSN: 23673370; ISBN: 978-981162421-6
Format
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Online
Language
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English
Type
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Conference paper
Coverage
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Savitha S., Department of Computer Science and Engineering, Christ University, Bangalore, India; Ravichandran S.K., Department of Computer Science and Engineering, Christ University, Bangalore, India
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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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Perceptive VM Allocation in Cloud Data Centers for Effective Resource Management
Subject
The topic of the resource
Cloud Computing; Parallelization; Prediction Model; Resource Management; VM Allocation
Description
An account of the resource
Virtual Machine allocation in cloud computing centers has become an important research area. Efficient VM allocation can reduce power consumption and average response time which can benefit both the end users as well as the cloud vendors. This work presents a perceptive priority aware VM allocation policy named P-PAVA algorithm, which takes into account the priority of an application along with its compute, memory and bandwidth requirement. The algorithm performs allocation of the applications based on the priority it gets using a machine learning based prediction model. Furthermore, to reduce the overhead of the allocation algorithm, parallelization is employed before assigning various workloads. To achieve this, the algorithm employs the First fit technique as a baseline for the requests allocation with a criteria as low priority. When compared to the state of the art algorithm for VM allocation for priority aware applications, P-PAVA performs better on several criteria such as average response time, execution time and power consumption. 2021 IEEE.
Creator
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Savitha S.; Salvi S.
Source
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2021 6th International Conference for Convergence in Technology, I2CT 2021
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Date
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2021-01-01
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<a href="https://doi.org/10.1109/I2CT51068.2021.9417960" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/I2CT51068.2021.9417960</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106470103&doi=10.1109%2FI2CT51068.2021.9417960&partnerID=40&md5=7609a0f44b36449ece544a912ad90478" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106470103&doi=10.1109%2fI2CT51068.2021.9417960&partnerID=40&md5=7609a0f44b36449ece544a912ad90478</a>
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ISBN: 978-172818876-8
Format
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Online
Language
A language of the resource
English
Type
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Conference paper
Coverage
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Savitha S., Christ University, Department of Computer Science Engineering, Bangalore, India; Salvi S., National Institute of Technology Karnataka, Department of Information Technology, Surathkal, India
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Title
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Faculty Publications
Article
Faculty Publications -Articles
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Savitha, S.; Rajiv Kannan, A.; Logeswaran, K.
Title
A name given to the resource
Hybrid feature optimization and radial basis function networks for cardiovascular disease prediction
Date
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01-01-2026
Source
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Biomedical Signal Processing and Control;Volume;117;Issue;;Article No.;109606;
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<a href="https://doi.org/10.1016/j.bspc.2026.109606" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.bspc.2026.109606</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105027733541?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105027733541?origin=resultslist</a>
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Savitha S., Department of CSE, K.S.R. College of Engineering, Tamil Nadu, Tiruchengode, 637215, India; Rajiv Kannan A., Department of CSE, K.S.R. College of Engineering, Tamil Nadu, Tiruchengode, 637215, India; Logeswaran K., Department of AI and Data Science Engineering, School of Engineering and Technology, CHRIST University, Kengeri, Karnataka, Bangalore, 560074, India
Description
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The study addresses the critical challenge of accurately predicting cardiovascular disease (CVD), a leading cause of mortality worldwide, where early diagnosis is crucial for effective intervention. Traditional models often struggle with high-dimensional data, imbalanced classes, and nonlinear feature interactions, limiting prediction reliability. Motivated by these gaps, this research proposes a hybrid methodology integrating Harris Hawks Search (HHS) for feature optimization with Radial Basis Function Networks (RBFN) to enhance CVD risk assessment. The HHS algorithm efficiently selects key predictive features such as chest pain type and number of vessels, reducing dimensionality while preserving vital information. Trained on optimized features, the RBFN classifier achieved superior performance with 92.1% accuracy, high sensitivity, and specificity, surpassing conventional models like Logistic Regression (81.2%) and Random Forest (86.7%). Ablation studies confirm each component's contribution, with significant gains validated statistically (p < 0.05). The hybrid model also offers computational efficiency with training times around 31.7 s. Future work aims to validate this approach on diverse, larger datasets and integrate it into real-time clinical decision support systems, advancing personalized, interpretable, and efficient cardiovascular healthcare tools. 2026 Elsevier Ltd
Subject
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Cardiovascular disease prediction; Harris Hawks Search; Hybrid feature optimization; Metaheuristic feature selection and machine learning; Radial basis function networks
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Elsevier Ltd
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ISSN: 17468094;
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English
Type
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Article
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online
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Book Chapter
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Faculty Publications- Book Chapter
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Effectiveness of gamification in facilitating microlearning for gen Z
Description
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This chapter offers a thorough examination of the uses, advantages, and difficulties of gamification in higher education. In contrast to game-based learning, gamification uses specific game features to improve the learning experience. This chapter investigates the use of gamification to engage and inspire Generation Z (Gen Z) pupils with the goal of enhancing their academic performance. It underlines the necessity for game development that increases motivation and engagement in educational settings and highlights the measurement of student progress based on completed activities. Effective instructional approaches are crucial in a time where there is a constant stream of information and people have short attention spans. A promising approach to overcoming these difficulties in both online and offline education utilizing ICT technologies is offered as gamified microlearning, which combines microlearning and games. 2024, IGI Global.
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Savithri M.; Philip S.; Sharma T.; Singh N.K.
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Global Perspectives on Micro-Learning and Micro-Credentials in Higher Education, pp. 137-158.
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IGI Global
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2024-01-01
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<a href="https://doi.org/10.4018/979-8-3693-0343-6.ch009" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-0343-6.ch009</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189061226&doi=10.4018%2F979-8-3693-0343-6.ch009&partnerID=40&md5=e74e9ab5b5d9e0cbec713eb2be944d8a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189061226&doi=10.4018%2f979-8-3693-0343-6.ch009&partnerID=40&md5=e74e9ab5b5d9e0cbec713eb2be944d8a</a>
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ISBN: 979-836930345-0; 979-836930343-6
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Online
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English
Type
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Book chapter
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Savithri M., Christ University, India; Philip S., Christ University, India; Sharma T., Christ University, India; Singh N.K., Christ University, India
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Faculty Publications
Book Chapter
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Savithri, M.; Arul Selvan Gnanamonickam, A.
Title
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Deep learning architectures for multimodal fusion
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01-01-2025
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Multimodal Learning Using Heterogeneous Data;pp.57-68
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<a href="https://doi.org/10.1016/B978-0-443-27528-9.00004-8" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-27528-9.00004-8</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032924333?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032924333?origin=resultslist</a>
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Savithri M., Department of Data Science, Christ University, Karnataka, Bengaluru, India; Arul Selvan Gnanamonickam A., Department of Software System and Computer Science (PG), KG College of Arts and Scienc, Tamil Nadu, Coimbatore, India
Description
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The advancement in technology during the recent years has provided deep learning technology as an emerging and powerful paradigm which can be used for processing and understanding complex data across various domains. Multimodal fusion is integrating the information that is collected from various sources or modalities, which requires a comprehensive understanding of data like autonomous driving, medical diagnosis, etc. In this chapter, we will explore the various advanced deep learning architectures that have been specially designed based on the multimodal fusion. The various challenges that are being faced in multimodal data, which include heterogeneity, noise, reliability of data, etc. Various deep learning architectures that are built to address the various challenges, like convolutional neural networks, recurrent neural networks, are reviewed, and the suitability of the fusion strategies is highlighted. The various techniques that are used for combining the information from disparate modalities, like early fusion, late fusion, and hybrid approaches, are also discussed with their pros and cons. Various real-time applications in the field of healthcare, multimedia, robotics, etc., are demonstrated based on the impact of the architectures. Finally, the potential of deep learning architecture based on the revolutionary multimodal fusion will be discussed. 2026 Elsevier Inc. All rights reserved.
Subject
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convolutional neural networks; deep learning; explainable AI (XAI); Multimodal fusion; multimodal generative adversarial networks; recurrent neural networks
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Elsevier
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ISBN: 978-044327528-9; 978-044327529-6;
Language
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English
Type
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Book chapter
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Faculty Publications
Book Chapter
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Savithri, M.; Vijayalakshmi, S.; SelvanGnanamonickam, A. Arul; Shobana, V.
Title
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Smart and Secured Ways of E-Payment: Design of New Frameworks
Date
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01-01-2025
Source
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Power Systems;Volume;Part F914;pp.419-433
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<a href="https://doi.org/10.1007/978-3-031-89459-6_18" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-89459-6_18</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105017993650?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105017993650?origin=resultslist</a>
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Savithri M., Department of Data Science, Christ University, Bengaluru, India; Vijayalakshmi S., Department of Data Science, Christ University, Bengaluru, India; SelvanGnanamonickam A.A., Department of Software Systems, KG College of Arts and Science, Coimbatore, India; Shobana V., Department of Computer Science with Cyber Security, Dr. N.G.P. Arts and Science College, Coimbatore, India
Description
An account of the resource
The rapid development in technology and the rapid growth of e-commerce have paved the way for changes in the method of payment. It also gave rise to various hazards while making the e-payment when compared to the normal or standard payment methods it is necessary to make a secured payment. Various initiatives are taken by the government for providing a secure payment system which will be more useful for all the commercial activities done through online. Already various e-payment systems are used by the consumers for paying the amount for the materials purchased. The increasing need of foreign exchange with an effective and efficient electronic payment system is required for making the low value payment. The framework that is been used in the global market and also in virtual marketplace require a complete legal structure which should also have impact on the economy of the mediaeval trade. Rapid development of e-commerce during the recent years has made more changes in the financial and non-financial transactions. In e-commerce, the payment gateway plays an important role in the exchange in ensuring that the transactions occur without any disputes and also maintains the security of the system. Most of the payment gateways used in the e-commerce are provided by rusted third party who will provide monetary information. Due to the increased use of e-commerce and online payment system, there is also any increase in security breaches during the past few years. So, it is necessary to build a new framework that will provide a secured platform for the e-payment system through which the consumers can directly connect to their merchants securely. Most of the third-party providers are also asking for the identity of the customers while making the payment which might even have change of loss of person information of the customer. The new framework should contain an improved security and the data collected should be confident, proper authentication method should be used, and availability of the data and integrity of the data should be maintained. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Subject
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Block chain; Contactless Payment; Crypto currency; Electronic Payment Systems; Encryption; Fraud Detection; Risk Management; Smart Payment; Two-factor Authentication
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Springer Science and Business Media Deutschland GmbH
Relation
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ISSN: 16121287;
Language
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English
Type
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Book chapter
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Editorials
Editorial
Faculty Publications- Editorial
Dublin Core
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Title
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Women at work: The cultural and creative industries
Description
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[No abstract available]
Creator
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Sawhney R.
Source
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Studies in South Asian Film and Media, Vol-11, No. 2, pp. 167-172.
Publisher
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Intellect Ltd.
Date
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2020-01-01
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<a href="https://doi.org/10.1386/safm_00026_2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1386/safm_00026_2</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102821944&doi=10.1386%2Fsafm_00026_2&partnerID=40&md5=e5e979da122f2542ee8b489484221718" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102821944&doi=10.1386%2fsafm_00026_2&partnerID=40&md5=e5e979da122f2542ee8b489484221718</a>
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ISSN: 17564921
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Online
Language
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English
Type
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Editorial
Coverage
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Sawhney R., Dept. of English and Cultural Studies, Christ University, Bangalore, India
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Title
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Book Chapter
Book Chapter
Faculty Publications- Book Chapter
Dublin Core
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Classification of financial news articles using machine learning algorithms
Subject
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Classifications; n-grams; Stock market; Support vector machine; Text mining
Description
An account of the resource
The opinion helps in determining the direction of the stock market. Information hidden in news articles is an information treasure which needs to be extracted. The present study is conducted to explore the application of text mining in binning the financial articles according to the opinion expressed inside them. It is discovered that using the tri-n-gram feature extraction process in conjugation with Support Vector machines increases the reliability and precision of the binning process. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.
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Saxena A.; Banik A.; Saswat C.; Joan Jose M.; Bhagat V.
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Lecture Notes in Networks and Systems, Vol-132, pp. 193-199.
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Springer
Date
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2021-01-01
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<a href="https://doi.org/10.1007/978-981-15-5309-7_20" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-5309-7_20</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090375590&doi=10.1007%2F978-981-15-5309-7_20&partnerID=40&md5=547939bc37d8157d9d15e0a7debf0c17" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090375590&doi=10.1007%2f978-981-15-5309-7_20&partnerID=40&md5=547939bc37d8157d9d15e0a7debf0c17</a>
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ISSN: 23673370
Format
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Online
Language
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English
Type
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Book chapter
Coverage
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Saxena A., CHRIST (Deemed to be University), Bengaluru, India; Banik A., CHRIST (Deemed to be University), Bengaluru, India; Saswat C., CHRIST (Deemed to be University), Bengaluru, India; Joan Jose M., CHRIST (Deemed to be University), Bengaluru, India; Bhagat V., CHRIST (Deemed to be University), Bengaluru, India
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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Title
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Reading behind the tweets: A sentiment Clustering Approach
Subject
The topic of the resource
Crude oil; prices; sentiment analysis; topic modeling; tweets
Description
An account of the resource
Market sentiment influence crude oil future prices in direct or indirect way. In order to measure the polarity of market sentiment various techniques has been deployed by industry and academia alike. This pilot study successfully introduced two instruments, namely topic modeling and Sentiment clustering, to unearth the prevailing sentiments behind crude oil future pricesThree main conclusions that can be drawn from empirical results are. First, the K-Means clustering algorithm is an effective technique for sentiment clustering compared to Louveian and MDS clustering techniques. Second sentiment polarity-related positive sentiments have shown more variations in comparison to neutral and negative sentiments. Third It is possible to extract the keywords related to essential factors influencing crude oil prices using the LDA technique under topic modeling 2022 IEEE.
Creator
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Saxena A.; Bhagat V.; Mahajan J.
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2022 International Conference on Advanced Computing Technologies and Applications, ICACTA 2022
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Institute of Electrical and Electronics Engineers Inc.
Date
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2022-01-01
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<a href="https://doi.org/10.1109/ICACTA54488.2022.9753107" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICACTA54488.2022.9753107</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129202605&doi=10.1109%2FICACTA54488.2022.9753107&partnerID=40&md5=84aa518054ccdd6d5d63beb342d87b3c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129202605&doi=10.1109%2fICACTA54488.2022.9753107&partnerID=40&md5=84aa518054ccdd6d5d63beb342d87b3c</a>
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ISBN: 978-166549515-8
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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
Saxena A., Christ University, India; Bhagat V., Christ University, India; Mahajan J., Christ University, India
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
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Title
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Insurance Data Analysis with COGNITO: An Auto Analysing and Storytelling Python Library
Subject
The topic of the resource
auto-analysis; automation; encoding; Feature engineering; NLP; pre-processing; problem solving; storytelling
Description
An account of the resource
Data pre-processing has taken an enhanced role with the advent of Machine learning. It is a vital element that forms the encore of the data science and business analytics process. Data pre-processing involves generating descriptive statistical summary, data cleaning, and data manipulation based on inputs gained after the initial analysis. Of late, it has been observed that data science practitioners spend 45% to 50% of their time cleaning and processing the data. Much time can be saved if the data transformation process can be automated. The COGNITO framework helps in performing the automated feature engineering and data storytelling of the dataset based on end-user discretion. The present work discusses the process and results obtained when automated feature engineering was performed on an insurance dataset using COGNITO. 2021 IEEE.
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Saxena A.; Bhagat V.V.; Robins B.
Source
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2021 International Conference on Intelligent Technologies, CONIT 2021
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Institute of Electrical and Electronics Engineers Inc.
Date
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2021-01-01
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<a href="https://doi.org/10.1109/CONIT51480.2021.9498523" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CONIT51480.2021.9498523</a>
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ISBN: 978-172818583-5
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
Saxena A., Christ University, Management Studies, Bangalore, India; Bhagat V.V., Christ University, Data Science, Bangalore, India; Robins B., Data Science Cleverinsight Inc., Bangalore, India
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Book Chapter
Book Chapter
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Title
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The Intellectual Structure of Application of Artificial Intelligence in Forecasting Methods: A Literature Review using Bibliometric Thematic Analysis
Description
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Crude oil is a valuable asset class which forms the nucleus of the energy core of the transport sector for any country. According to report [1], crude oil helps in meeting 93% of energy needs for the transportation sector globally. It has been projected across various forums that crude oil along with coal and natural gas is going to satisfy world energy needs for the forthcoming years. Consequently, it has been observed that fluctuations in crude oil prices tilt the economies of scale across the world. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar.
Creator
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Saxena A.; Mahajan J.; Bhagat V.; Subha M.V.; Paul B.P.; Jain V.
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Artificial Intelligence in Forecasting: Tools and Techniques, pp. 283-303.
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CRC Press
Date
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2024-01-01
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<a href="https://doi.org/10.1201/9781003399292-18" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003399292-18</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195934461&doi=10.1201%2F9781003399292-18&partnerID=40&md5=2005295d66bbeaa2f32b1306d9cf4155" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195934461&doi=10.1201%2f9781003399292-18&partnerID=40&md5=2005295d66bbeaa2f32b1306d9cf4155</a>
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ISBN: 978-104005150-4; 978-103250615-9
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
Saxena A., Christ University, India; Mahajan J., Christ University, India; Bhagat V., Christ University, India; Subha M.V., Anna University, India; Paul B.P., Christ Academy, India; Jain V., Marwadi University, India
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Articles
Article
Faculty Publications -Articles
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Designing an artificial intelligence-enabled large language model for financial decisions
Subject
The topic of the resource
Artificial intelligence; Financial analytics; Financial decision; FinSageNet; Large language models
Description
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Purpose: Artificial intelligence (AI) has profoundly reshaped financial decision-making, introducing a paradigm shift in how institutions and individuals navigate the complex finance landscape. The study evaluates the significant impact of integrating advanced AI and large language models (LLMs) in financial decision analytics. Design/methodology/approach: The study offers FinSageNet, a novel framework designed and tested to harness the potential of LLMs in financial decisions. The framework excels in handling and analyzing large volumes of numerical and textual data through advanced data mining techniques. Findings: FinSageNet demonstrates exceptional text summarization capabilities, outperforming models like FLAN and GPT-3.5 in Rouge score metrics. The proposed model has shown more accuracy than generic models. Originality/value: The study emphasizes the significance of consistently updating models and adopting a comprehensive approach to integrating AI into financial decisions. This study improves our understanding of how artificial intelligence transforms financial analytics and decision-making processes. 2025, Emerald Publishing Limited.
Creator
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Saxena A.; Rishi B.
Source
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Management Decision
Publisher
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Emerald Publishing
Date
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2025-01-01
Identifier
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<a href="https://doi.org/10.1108/MD-02-2024-0305" target="_blank" rel="noreferrer noopener">https://doi.org/10.1108/MD-02-2024-0305</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216339102&doi=10.1108%2FMD-02-2024-0305&partnerID=40&md5=d612ef79f97e1f9611a0bb2ca6d15a18" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216339102&doi=10.1108%2fMD-02-2024-0305&partnerID=40&md5=d612ef79f97e1f9611a0bb2ca6d15a18</a>
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Restricted Access
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ISSN: 251747
Format
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Online
Language
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English
Type
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Article
Coverage
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Saxena A., School of Business and Management, Christ University, Bangalore, India; Rishi B., School of Management and Entrepreneurship, Shiv Nadar Institution of Eminence Deemed to be University, Gautam Buddha Nagar, India
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Title
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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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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Stock Market Trend Analysis on Indian Financial News Headlines with Natural Language Processing
Subject
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Data Mining; Machine Learning; NLP; Sentiment Analysis; stock prediction; Trend Analysis
Description
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Predicting the stock movement in the real-time scenario has been the most challenging and sophisticated in business. This business is affected by several factors from physical to psychological as well as rational to irrational. So far only few aspects have been taken into account while breaking down the conclusion. Implementing sentiment analysis, a subfield of Natural Language Processing (NLP), from the news, social media or financial document, investors decide whether they should invest for the company. The results have shown a significant and a feasible method for predicting the stock market trend with higher accuracy. The current research has mainly focus on finding the sentiment score from the news headlines and finding the hidden trend from it. Further the trading signals are generated based on the prevailing trend and trends are executed by the automated trading system. Using this algorithm, traders can reduce the manual intervention in the buy and sell decisions related to the stock market. 2021 IEEE.
Creator
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Saxena A.; Vijay Bhagat V.; Tamang A.
Source
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2021 Asian Conference on Innovation in Technology, ASIANCON 2021
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Date
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2021-01-01
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<a href="https://doi.org/10.1109/ASIANCON51346.2021.9544965" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ASIANCON51346.2021.9544965</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117622721&doi=10.1109%2FASIANCON51346.2021.9544965&partnerID=40&md5=190562bf484e813b30b679786d2cbf51" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117622721&doi=10.1109%2fASIANCON51346.2021.9544965&partnerID=40&md5=190562bf484e813b30b679786d2cbf51</a>
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ISBN: 978-172818583-5
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Online
Language
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English
Type
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Conference paper
Coverage
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Saxena A., Christ (Deemed to Be University), Bangalore, India; Vijay Bhagat V., Christ (Deemed to Be University), Bangalore, India; Tamang A., Independent Consultant, Bangalore, 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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Framing and control for sustainability of industries
Subject
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Data analytics; Recycling; Regulatory framework; Sustainability; Sustainability fund; Zero footprint
Description
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Purpose: The paper attempts to frame the challenge of managing the transition to a sustainable economy by way of a conceptual model consisting of a zero-footprint regulatory regime and a sustainability fund. Design/methodology/approach: A conceptual model of the sustainable industrial revolution has been developed based on the learnings from industries such as originators (mining), farming, pharmaceuticals, pesticides and chemicals and long-lasting artefacts against an overall perspective. Findings: It is suggested to have an institutional structural mechanism in place to ensure that footprint is minimized through recycling including refurbishing, resale or transformation. This includes management of recycling businesses through execution of a zero-waste regulatory regime that will build and use a sustainability fund. Research limitations/implications: The limitations of the paper are arising out of the topic being an issue of gigantic proportions with immense complexity. An attempt has been made to bring out the inescapability and the imperative of a sustainable industrial revolution. Practical implications: This paper presents practical aspects such as collusion between trash and recycling businesses, land use and social aspects of criticality of public support. If implemented, the suggested model can make a paradigm shift in the way firms, industry and governments can handle the challenge of sustainability. Originality/value: The value of this conceptual paper lies in an attempt to extend the learning organization framework to the concept of a regulatory model for sustainability that is not limited to the definition of a firm but stands extended to industries and to the economics, land use and demographics of the planet. 2021, Emerald Publishing Limited.
Creator
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Saxena R.; Kishore S.; Srivastava V.
Source
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Technological Sustainability, Vol-1, No. 1, pp. 64-81.
Publisher
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Emerald Publishing
Date
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2022-01-01
Identifier
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<a href="https://doi.org/10.1108/TECHS-08-2021-0003" target="_blank" rel="noreferrer noopener">https://doi.org/10.1108/TECHS-08-2021-0003</a>
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Restricted Access
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ISSN: 27541312
Format
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Online
Language
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English
Type
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Article
Coverage
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Saxena R., Cobot Systems, Bengaluru, India; Kishore S., Indian Railways, Rail Wheel Factory, Bengaluru, India; Srivastava V., School of Business and Management, Christ University, Bengaluru, India