121
20
16481
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Title
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Book Chapter
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Faculty Publications- Book Chapter
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Analytics Enabled Decision Making Tracing the Journey from Data to Decisions
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Analytics; Business analytics; Data science; Data-based decision making; Decision making; Model building
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In the current business environment, which is greatly dynamic and competitive, business organizations are continually striving for expanding their competence and financial performance through improving almost every facet of their business--product/service quality, customer satisfaction, customer retention, productivity, line filling strategies, and others. In this sense, success and failure of organizations depend on the extent of precision of their decisions. Organizations are engaged with data to extract insights, identify trends and make decisions at different levels; and also, many of them learn how to utilize the power of data. Analytics can enable them to derive conclusions, make predictions, and ascertain actionable insights in a contextual and time-bound manner. It helps to examine data from multiple perspectives and gives visualizations by using different frameworks and platforms such as IBM Watson, Tableau, and R. The chapter presents the role of analytics in decision-making processes and assess the effectiveness of decisions upon their implementation, so the corrective measures can also be inserted. As decision making is a continuous business process, analytics accelerates it and gives organizations a pace to keep updated with changing business scenarios. Thus, this chapter presented a decision-making framework exhibiting how decision-making functions as an ongoing process. Different contexts and cases have been used to establish the relevance of each step of the framework. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
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Sharma V.; Poulose J.; Maheshkar C.
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Analytics Enabled Decision Making, pp. 1-22.
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Springer Nature
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2023-01-01
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<a href="https://doi.org/10.1007/978-981-19-9658-0_1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-19-9658-0_1</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171505493&doi=10.1007%2F978-981-19-9658-0_1&partnerID=40&md5=5103dbf427e5f911b663682e40f15441" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171505493&doi=10.1007%2f978-981-19-9658-0_1&partnerID=40&md5=5103dbf427e5f911b663682e40f15441</a>
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ISBN: 978-981199658-0; 978-981199657-3
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Online
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English
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Book chapter
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Sharma V., Symbiosis Centre for Management and Human Resource Development (SCMHRD), Symbiosis International (Deemed University), Pune, India; Poulose J., East Nimar Society for Education, Khandwa, India; Maheshkar C., School of Business and Management, CHRIST (Deemed to Be University), Delhi NCR, India
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Title
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Articles
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Faculty Publications -Articles
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Title
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Analytics in e-learning
Subject
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Data analytics; E learning; Higher education; Predictive analytics; Principle component analysis (PCA)
Description
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Predictive analytics play an important role in the evolving dynamics of higher education. There has been a steady up rise in use of technology in the field of education. e-learning is seen as a futuristic approach of learning. Hence, the study of factors influencing success in e-learning courses is relevant to the current scenario. Use of predictive analytics in virtual learning environment would provide insight on learning patterns of students. The learning data available in the traditional teaching environment is different from the one, which is available from virtual learning. This paper tries to identify various attributes associated with e learning which can help in making the learning process effectual. International Research Publication House.
Creator
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Tom S.T.; Tulasi B.
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International Journal of Engineering Research and Technology, Vol-11, No. 2, pp. 319-332.
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International Research Publication House
Date
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2018-01-01
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<a href="" target="_blank" rel="noreferrer noopener"></a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051054879&partnerID=40&md5=e6addb6ede1ddb7dff40cc6a099737b3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051054879&partnerID=40&md5=e6addb6ede1ddb7dff40cc6a099737b3</a>
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ISSN: 9743154
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Online
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English
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Article
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Tom S.T., Department of Computer Science, CHRIST (Deemed to be University, Bengaluru, Karnataka, India; Tulasi B., Department of Computer Science, CHRIST (Deemed to be University, Bengaluru, Karnataka, India
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https://archives.christuniversity.in/files/original/a1977934c16194328ff5ddddbd1b9871.jpg
0b6dfeeadbd8dbf97f5aef738454f6b9
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Patents
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Patent
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Patent
Patent
Patents
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Title
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Analyze the many business-to-consumer (B2C) marketing strategies that can be implemented on social media platforms /
Subject
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Computer Science
Description
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Patent Number: 202241036460, Applicant: Dr.K Abirami Devi.
How do business-to-consumer and business-to-business social media platforms differ How do you go about the process of developing relevant social material for a community that includes end-users as well as other businesses Learn the ins and outs of an effective social media marketing strategy for both business to consumer and business to business. Transform your social media platforms into marketing tools that increase both brand exposure and sales. Even if your company does not have a social media plan, you probably still make use of social media in some capacity because it is cost-free, has the potential to be quite effective, and can be simplified.
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Chaurasia, Amrita.
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<a href="https://drive.google.com/file/d/1nuqEBwSdXan9jNFFjXD2TjLvpM_DqDJ8/view" target="_blank" title="Analyze the many business-to-consumer (B2C) marketing strategies that can be implemented on social media platforms /" rel="noreferrer noopener">https://drive.google.com/file/d/1nuqEBwSdXan9jNFFjXD2TjLvpM_DqDJ8/view</a>
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Intellectual Property India
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2022-05-22
Language
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English
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Patent
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Title
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Articles
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Faculty Publications -Articles
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Analyzing and optimizing the usability of website access
Subject
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Frequency; Latency; Recency; Replacement Policy; Web; Web Cache; Web Log
Description
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The world wide web (WWW) plays a significant role in information sharing and distribution. In web-based information access, the speed of information retrieval plays a critical role in shaping the web usability and determining the user satisfaction in accessing webpages. To deal with this problem, web caching is used. The problem with the present web caching system is that it is very hard to recognize webpages that are to be accessed and then to be cached. This is forced by the fact that there are broad categories of users and each one having their own preferences. Hence, it is decided to propose a novel approach for web access pattern generation by analyzing the web log file present in the proxy server. Further, it tries to propose a novel hybrid policy called popularity-aware modified least frequently used (PMLFU) that best suits for the current proxy-based web caching environment. It combines features such as frequency, recency, popularity, and user page count in decision-making policy. The performance of the proposed system is observed using real-time datasets, empirically using IRCACHE datasets. 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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Sathiyamoorthi V.; Jayapandian N.; Gnana Prakasi O.S.; Kanmani P.; Vaithiyanathan R.; Prasanth Rao A.
Source
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International Journal of Web Portals, Vol-12, No. 2, pp. 22-40.
Publisher
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IGI Global
Date
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2020-01-01
Identifier
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<a href="https://doi.org/10.4018/IJWP.2020070102" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/IJWP.2020070102</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093656129&doi=10.4018%2FIJWP.2020070102&partnerID=40&md5=b8727ca2ecc3378a1ca199a2edde3392" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093656129&doi=10.4018%2fIJWP.2020070102&partnerID=40&md5=b8727ca2ecc3378a1ca199a2edde3392</a>
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ISSN: 19380194
Format
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Online
Language
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English
Type
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Article
Coverage
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Sathiyamoorthi V., Sona College of Technology, India; Jayapandian N., Christ University, Deemed, India; Gnana Prakasi O.S., Christ University, Deemed, India; Kanmani P., Christ University, Deemed, India; Vaithiyanathan R., Dayananda Sagar University, India; Prasanth Rao A., Anurag Group of Institutions, India
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Articles
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Title
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Analyzing blockchain-based supply chain resilience strategies: resource-based perspective
Subject
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Blockchain; Micro; Resource-based view; Small and medium-sized enterprises (MSMEs); Supply chain disruptions; Supply chain resilience; Textile industry
Description
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Purpose: This research tries to find the blockchain-based resilience strategies that can help the supply chains of micro, small, and medium-sized enterprises (MSMEs) to recover from the disruptions and work effectively in a resource-based view perspective. Design/methodology/approach: Eight broad strategies and 32 sub-strategies are identified from the literature review. Delphi study was carried out, and detailed discussion with 16 experts helped in finalizing these strategies. Further, the best-worst method (BWM) prioritized these strategies. Findings: The findings suggests that building social capital, improving coordination capabilities, sensitivity towards market, flexibility in process and production, reduction in process and lead time,and having a resource efficiency and redundancy are the top strategies on which the top management should focus to overcome the situations of disruptions and enhance performance of MSMEs. Practical implications: The blockchain-based strategies will enable the companies in tracing the products from the company to customers. Further, the customers will be able to identify their manufacturers, the raw materials used in manufacturing, and the life and quality of raw used materials. Altogether the textile industry will become more sensitive toward environmental practices. Originality/value: The previous research has not identified and evaluated the blockchain-based resilience strategies, and therefore this study tries to fill this gap. This study used a smaller sample from the experts, so the results may vary if the larger data set is used and hypothesis testing can be done. 2023, Emerald Publishing Limited.
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Nagariya R.; Mukherjee S.; Baral M.M.; Chittipaka V.
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International Journal of Productivity and Performance Management, Vol-73, No. 4, pp. 1088-1116.
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Emerald Publishing
Date
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2024-01-01
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<a href="https://doi.org/10.1108/IJPPM-07-2022-0330" target="_blank" rel="noreferrer noopener">https://doi.org/10.1108/IJPPM-07-2022-0330</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159714283&doi=10.1108%2FIJPPM-07-2022-0330&partnerID=40&md5=2d8e9c7e3201c32dd3cfaf15ca0df76e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159714283&doi=10.1108%2fIJPPM-07-2022-0330&partnerID=40&md5=2d8e9c7e3201c32dd3cfaf15ca0df76e</a>
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ISSN: 17410401
Format
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Online
Language
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English
Type
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Article
Coverage
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Nagariya R., School of Business and Management, CHRIST (Deemed to be University), Bangalore, India; Mukherjee S., Department of Operations, GITAM School of Business, GITAM, Visakhapatnam, India; Baral M.M., Department of Operations, GITAM School of Business, GITAM, Visakhapatnam, India; Chittipaka V., School of Management, Indira Gandhi National Open University, New Delhi, India
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Title
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Faculty Publications
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Faculty Publications -Articles
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Colaco, Vembly Megna; Fernandes, Evelyn Savia; Kassetty, Nikhil; Chippagiri, Srinivas; Prova, Nuzhat Noor Islam; Devesh, Sonal; Naik, Nithesh
Title
A name given to the resource
Analyzing Corporate Social Responsibility (CSR) Practices and Ethical Leadership in Promoting Sustainable Business: A Structured Equation Modelling Approach
Date
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01-01-2025
Source
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Engineered Science;Volume;36;Issue;;Article No.;1638;
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<a href="https://doi.org/10.30919/es1638" target="_blank" rel="noreferrer noopener">https://doi.org/10.30919/es1638</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105024339838?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105024339838?origin=resultslist</a>
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Colaco V.M., Department of Economics, Government College of Commerce and Economics, Borda (Autonomous), Goa University, Goa, Margao, 403601, India; Fernandes E.S., Department of Sociology, Government College of Arts, Science and Commerce, Goa, Quepem, 403705, India; Kassetty N., Georgia, Atlanta, 30002, United States; Chippagiri S., Seattle, 98102, WA, United States; Prova N.N.I., Department of Computer Science and Information Systems, Seidenberg School of Computer Science and Information Systems, Pace University, New York City, 10038, United States; Devesh S., Department of Business and Management, Christ University, Yeshwanthpur Campus, Karnataka, Bengaluru, 560073, India; Naik N., Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Karnataka, Manipal, 576104, India
Description
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This study investigates the pivotal role of Corporate Social Responsibility (CSR) practices and ethical leadership in promoting sustainable business development across diverse regions of India, specifically focusing on Goa, Kerala, and Gujarat. Employing a quantitative approach, data was collected from a sample of 300 respondents through a structured questionnaire using a five-point Likert scale. The survey assessed perceptions of CSR initiatives, the influence of ethical leadership, and their combined effect on sustainability outcomes within the regional business landscape. To analyze the data, Partial Least Squares Structural Equation Modeling (PLS-SEM) was conducted using Smart PLS software, enabling the evaluation of complex relationships and the validation of hypotheses. The findings reveal a strong, positive correlation between ethical leadership and CSR engagement, both of which significantly contribute to sustainable business practices. The study highlights that organizations adopting responsible and transparent business practices, guided by ethical leadership, are more likely to enhance their long-term sustainability and contribute to regional development. Furthermore, the research underscores the growing relevance of integrating CSR into core business strategies to align with sustainable development goals (SDGs). By incorporating a machine learning perspective, the study also suggests avenues for future research in predictive modeling and CSR impact assessment. This research adds value to the existing literature by contextualizing CSR within Indias varied socio-economic environments and offers practical insights for policymakers, business leaders, and sustainability advocates aiming to foster ethical and responsible business ecosystems across the country. The Author(s) 2025 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits the use, sharing, adaptation, distribution and reproduction in any medium or format, as long as appropriate credit to the original author(s) and the source is given by providing a link to the Creative Commons license and changes need to be indicated if there are any. The images or other third-party material in this article are included in the articles Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the articles Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Subject
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Corporate social responsibility; Ethical leadership; India; Smart PLS; Sustainable development
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Engineered Science Publisher
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ISSN: 2576988X;
Language
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English
Type
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Article
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All Open Access; Gold Open Access
Format
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online
-
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Title
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Bomble, Shivam Vijay; Nizar Banu, P.K.
Title
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Analyzing Deep Learning Architectures in Cotton Crop for Precision Disease Diagnosis
Date
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01-01-2025
Source
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2025 17th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2025;pp.337-344
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<a href="https://doi.org/10.1109/CICN67655.2025.11368201" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CICN67655.2025.11368201</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034688816?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034688816?origin=resultslist</a>
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Bomble S.V., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Nizar Banu P.K., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India
Description
An account of the resource
Cotton is an important cash crops worldwide, providing raw materials for the textile industry and is the basis of livelihood of millions of farmers. In India, it has an important place in the agricultural economy, which contributes significantly to both domestic consumption and export income. However, cotton production is highly sensitive to infection of various diseases and insects, such as bacterial scorching, powdery mildew and targeted spots, which can cause severe yield reduction and economic loss. Traditional disease management methods often depend on manual inspection, which is difficult to scale in time consuming, human error and large cultivated areas. Therefore, it is necessary to detect the initial and accurate detection of the disease to ensure plant health and maximize productivity. This study examines advanced intensive teaching methods for automatic cotton disease diagnosis, and compare the performance of VGG16 and ResNet18 architecture. Experimental results showed that the VGG16 model achieved verification accuracy of 99.69%, while ResNet18 achieved an accuracy of 99.58%. In addition, a real time forecasting interface was developed from the URL provided by the user to classify images of cotton leaves, making practical signs possible for use in the area. This research highlights effectiveness of deep learning in improving accurate agriculture, which helps in timely detection of diseases to reduce the loss of crops. 2025 IEEE.
Subject
The topic of the resource
Convolutional Neural Networks (CNN); Cotton Disease; Precision Agriculture; ResNet18; VGG16
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Relation
A related resource
ISBN: 979-833158733-8;
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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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
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Title
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Analyzing Dual-Stage Inverter Performance for Solar Grid Integration
Subject
The topic of the resource
Boost converter; Dual-stage inverter; Grid interaction; Photovoltaic; Utility grid
Description
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This paper presents a comprehensive analysis of the performance of dual-stage inverters in the context of solar grid integration through simulation. Dual-stage inverters are increasingly recognized for their potential to enhance the efficiency and reliability of solar power systems by mitigating grid disturbances and optimizing energy extraction. Through detailed simulation studies, this research evaluates key performance metrics such as grid stability, power quality, and energy conversion efficiency. The simulation environment enables the exploration of various operational scenarios and system configurations to assess the versatility and robustness of dual-stage inverter solutions. Furthermore, the study investigates the impact of control strategies and parameter variations on the overall performance of dual-stage inverters, providing valuable insights for system optimization and design. 2024 IEEE.
Creator
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Parimalasundar E.; Jayakumar S.; Sita H.; Muthukaruppasamy S.; Suresh K.; Kumar B.H.
Source
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Proceedings of International Conference on Circuit Power and Computing Technologies, ICCPCT 2024, pp. 669-674.
Publisher
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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/ICCPCT61902.2024.10673348" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCPCT61902.2024.10673348</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205600736&doi=10.1109%2FICCPCT61902.2024.10673348&partnerID=40&md5=9d7d7ed9c380c27eeee7679c1e530435" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205600736&doi=10.1109%2fICCPCT61902.2024.10673348&partnerID=40&md5=9d7d7ed9c380c27eeee7679c1e530435</a>
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ISBN: 979-835037281-6
Format
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Online
Language
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English
Type
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Conference paper
Coverage
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Parimalasundar E., Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Electrical and Electronics Engineering, Tirupati, India; Jayakumar S., Sri Sairam College of Engineering, Electronics and Communication Engineering, Bengaluru, India; Sita H., Mother Theresa Institute of Engineering and Technology, Electrical and Electronics Engineering, Palamaner, India; Muthukaruppasamy S., Velammal Institute of Technology, Electrical and Electronics Engineering, Chennai, India; Suresh K., Christ University, Electrical and Electronics Engineering, Bengaluru, India; Kumar B.H., Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Electrical and Electronics Engineering, Tirupati, India
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Title
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Faculty Publications
Article
Faculty Publications -Articles
Dublin Core
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Srivastava, Shefali; Shardeo, Vipulesh; Dwivedi, Ashish; Paul, Sanjoy Kumar
Title
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Analyzing enablers of artificial intelligence for decarbonization: implications for circular supply chains
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01-01-2026
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Annals of Operations Research;Volume;359;Issue;1;pp.953-980
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<a href="https://doi.org/10.1007/s10479-025-06843-x" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10479-025-06843-x</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018773848?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018773848?origin=resultslist</a>
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Srivastava S., Christ University, Bangalore, India; Shardeo V., FORE School of Management, New Delhi, India; Dwivedi A., Jindal Global Business School, O. P. Jindal Global University, Sonipat, India; Paul S.K., UTS Business School, University of Technology Sydney, Sydney, Australia
Description
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This study comprehensively explores the pivotal position that Artificial Intelligence (AI) enables on the advancement of decarbonization efforts, mainly in the context of Circular Supply Chains (CSCs). Employing a two-stage methodology, this study delves into identifying and analyzing the enablers essential for leveraging AI in the pursuit of decarbonization objectives. In the first stage, a literature review and an exploratory factor analysis are performed to discern the key enablers of AI for decarbonization initiatives. This process resulted in the identification of 15 significant enablers and categorization of enablers into environmental, organizational, institutional, and technological categories. Building upon the findings from the first stage, this study progresses to its second stage, wherein the Grey-Ordinal Priority Approach (G-OPA) is applied to analyze the identified enablers. The results indicate that adopting recyclable materials to enhance the efficiency of supply chains, emphasizing local production for recovery practices through advanced technology, and managing product life-cycle through intelligent and additive manufacturing technologies are the top three enablers. The application of the G-OPA enriches the robustness and comprehensiveness of the analysis, enabling an understanding of the complex interplay among the enablers. By clarifying the key enablers,business planners and designers can migrate from traditional linear supply chains to more sustainable CSCs through the careful implementation of enablers for decarbonization. The Author(s) 2025.
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Artificial intelligence; Circular supply chains; Decarbonization; Grey-ordinal priority approach
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Springer
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ISSN: 2545330;
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English
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Article
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All Open Access; Hybrid Gold Open Access
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online
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Faculty Publications
Conference Paper
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Verma, Adarsh Umang; Sharma, Vandana; Soran, Preety
Title
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Analyzing Financial Metrics: A Comparative Study of Salesforce and Microsoft Dynamics 365
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01-01-2025
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IEEE International Conference on "Computational, Communication and Information Technology", ICCCIT 2025;pp.128-135
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<a href="https://doi.org/10.1109/ICCCIT62592.2025.10927817" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCCIT62592.2025.10927817</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105002235382?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002235382?origin=resultslist</a>
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Verma A.U., Christ University, Department of Computer Science, NCR, Delhi, India; Sharma V., Christ University, Department of Computer Science, NCR, Delhi, India; Soran P., Christ University, Department of Computer Science, NCR, Delhi, India
Description
An account of the resource
The purpose of the study is, therefore, in the integration of six variables consisting of closing prices, daily returns, volatility, stock prices together with moving averages, trading volumes, and their moving averages: how these measure interrelates and what impacts they have toward market sentiment. Correlation and regression tests were carried out to ascertain whether the obtained findings were reliable enough to present robust relationships among the metrics of concern. Therefore, the obtained findings have wider implications for investors, as well as their investing decisions. In the first place, these findings permit one to detail and analyze in an in-depth manner the stock shares' behavior overtime and variations of the market so that trends within trading activity could be unearthed and understood long-term effects. 2025 IEEE.
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etc; Financial Metrics; Moving Averages; Stock Market Analysis; Trading Volumes; Volatility
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Institute of Electrical and Electronics Engineers Inc.
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A related resource
ISBN: 979-833151296-5;
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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Title
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Conference Papers
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Analyzing Job Satisfaction, Job Performance, and Attrition in International Business Machines Corporation through Python
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Attrition; Employee Performance; Job Involvement; Job Satisfaction Rating; Work-Life Balance
Description
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Since workers significantly impact the firm's operation, businesses invest heavily in them. They must deliver better and more excellent performance to compete with the increasing competition. Employee performance is becoming more and more important for business success and staying ahead of the competition, so companies are putting more money into things like training, growth centers, and careers. The target audience was the employees working in International Business Machines Corporation. The data was analyzed through the process of Exploratory Data Analysis using Python. There is a 0.002297 link between Job Satisfaction and Performance Rating, and a 0.002572 correlation between Work Life Balance and Performance Rating. The relationship between work-life balance and job involvement is -0.01462, indicating a negative impact on work-life balance for people who are heavily interested in their occupations. The study would help Human Resources Managers formulate their policies and understand the employees better in the current environment. Here, Job Satisfaction and Performance Rating served as mediators, and the findings show that their influence on Attrition is minimal at this firm. 2024 IEEE.
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Salunkhe H.A.; Khanna R.; Yawalkar P.; Birari A.
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TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/TQCEBT59414.2024.10545255" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT59414.2024.10545255</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204432233&doi=10.1109%2FTQCEBT59414.2024.10545255&partnerID=40&md5=d337208c1f19d34a864b6af4215da32d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204432233&doi=10.1109%2fTQCEBT59414.2024.10545255&partnerID=40&md5=d337208c1f19d34a864b6af4215da32d</a>
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ISBN: 979-835038427-7
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Online
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English
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Conference paper
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Salunkhe H.A., Christ University, Bangalore, India; Khanna R., Christ University, Bangalore, India; Yawalkar P., Christ University, Bangalore, India; Birari A., Christ University, Bangalore, India
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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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Analyzing Market Factors for Stock Price Prediction using Deep Learning Techniques
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FBPROPHET; Financial markets; FinBERT; GBDT; Lexical- Based; LSTM; MAE; MSE; Naive Bayes; RMSE; Stock price predictions
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This paper presents a comprehensive study on stock price predictions by integrating market factors and sentiment analysis of news headlines. The research is divided into two modules, each employing distinct methodologies to enhance the accuracy of stock price forecasts. In the first module, market factors are investigated using three advanced algorithms: Long Short-Term Memory (LSTM), Gradient Boosting Decision Trees (GBDT), and Facebook Prophet (FBPROPHET). These algorithms are evaluated based on metric scores such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The analysis focuses on predicting high and low values of market prices for the period from January to June 2021. The comparative assessment of these algorithms provides insights into their effectiveness in capturing market trends and making precise predictions. In the second module, the paper explores the impact of news headlines on stock prices by extracting sentiment using three distinct algorithms: lexical-based analysis, Naive Bayes, and FinBERT. The sentiment analysis aims to gauge the market sentiment reflected in news articles and assess its influence on stock price movements. Prediction accuracy is calculated for each algorithm, highlighting their strengths in capturing sentiment patterns. 2024 IEEE.
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Suresh K.; Sarkar A.; Rai A.; Vasishtha A.; Fernandes T.; Cecil Donald A.
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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024 - Proceedings, pp. 1837-1841.
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/I-SMAC61858.2024.10714754" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/I-SMAC61858.2024.10714754</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208572605&doi=10.1109%2FI-SMAC61858.2024.10714754&partnerID=40&md5=5ea5ded405cc0ef8facdc80c12150ac1" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208572605&doi=10.1109%2fI-SMAC61858.2024.10714754&partnerID=40&md5=5ea5ded405cc0ef8facdc80c12150ac1</a>
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ISBN: 979-835037642-5
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Online
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English
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Conference paper
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Suresh K., Christ University, Department of Computer Science, Bangalore, 560029, India; Sarkar A., Christ University, Department of Computer Science, Bangalore, 560029, India; Rai A., Christ University, Department of Computer Science, Bangalore, 560029, India; Vasishtha A., Christ University, Department of Computer Science, Bangalore, 560029, India; Fernandes T., Christ University, Department of Computer Science, Bangalore, 560029, India; Cecil Donald A., Christ University, Department of Computer Science, Bangalore, 560029, India
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Title
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Articles
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Title
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Analyzing online food delivery industries using pythagorean fuzzy relation and composition
Subject
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Choice of restaurants; Payment option; Pythagorean Fuzzy Relation; Quality of device; Speed of delivery
Description
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Food and beverages constitute a significant portion of the family expenditure, which motivates the food delivery companies in striving hard to meet the customer needs through their dynamic food delivery apps. The online food ordering system is one of the most profitable marketing strategies for restaurant businesses. The face of the restaurant industry has shifted from the traditional dine-in culture to takeaways, online ordering, and home deliveries. Digital technology and social media have a significant role in ensuring the efficiency and popularity of a food delivery app. The four essential factors for a food delivery company to satisfy the needs of the consumers in day to day life are choice of restaurants, speed of delivery, payment option and quality of service. The objective of this study is to discern and analyse these four essential factors adopted by the leading four food delivery companies and evaluate the perceptions of the consumers. The best online food delivering company is identified using Pythagorean Fuzzy Relation (PFR)and composition. The analysis concludes that Zomato food application is the best in consumers perception.The outcome of the survey is made more efficient by adopting a mathematical approach. Copyright IJHTS.
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Dinakaran U.
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International Journal of Hospitality and Tourism Systems, Vol-14, No. 2, pp. 36-45.
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Publishing India Group
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2021-01-01
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<a href="" target="_blank" rel="noreferrer noopener"></a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103154363&partnerID=40&md5=483e74240b5610aec3caaaef2a685007" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103154363&partnerID=40&md5=483e74240b5610aec3caaaef2a685007</a>
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ISSN: 9746250
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Online
Language
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English
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Article
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Dinakaran U., Department of Hotel Management, Christ (Deemed to be University), Bangalore, Karnataka, India
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Title
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Book Chapter
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Dublin Core
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Analyzing Risk-Return Trade-Offs Using ARCH and GARCH Models of the BRICS Countries
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ARCH; BRICS; GARCH; NIFTY; Volatility
Description
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This study investigates financial markets in BRICS nations (Brazil, Russia, India, China, and South Africa) from 2003 to 2023. It examines mean returns, volatility, skewness, and kurtosis, assessing normality and data stationarity. ARCH-GARCH models uncover conditional heteroskedasticity and volatility clustering. It also explores mean reversion and momentum effects in the Nifty and MOEX indices. Findings show negative, near-zero mean returns, except for SSEC, which is modestly positive. Serial correlation suggests past values impact current returns. Volatility varies, with MOEX and SSEC having higher levels. ARCH-GARCH models indicate volatility clustering and non-normal return distributions. Mean reversion and momentum effects are identified in Nifty and MOEX, benefiting investors, financial institutions, and policymakers. This research informs investment strategies, risk management, and financial forecasts in BRICS economies, contributing to the understanding of the global financial landscape and potential contagion effects. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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Shashidhar Yadav J.; Dhaku C.R.; Bhat S.R.; Jagadeesha G.T.; Manjunatha G.; Druvakumar M.
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Studies in Systems, Decision and Control, Vol-540, pp. 109-119.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/978-3-031-62656-2_11" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-62656-2_11</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201804347&doi=10.1007%2F978-3-031-62656-2_11&partnerID=40&md5=7ecf0f889d9c364662ec68b68d2cd347" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201804347&doi=10.1007%2f978-3-031-62656-2_11&partnerID=40&md5=7ecf0f889d9c364662ec68b68d2cd347</a>
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ISSN: 21984182
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Online
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English
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Book chapter
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Shashidhar Yadav J., Department of Commerce, MLA Academy of Higher Learning, Karnataka, Bengaluru, India, School of Business and Management, Christ University, Bangalore, India; Dhaku C.R., Department of Commerce, CHRIST (Deemed to be University), Bengaluru, India, School of Commerce, Finance and Accountancy, Christ University, Bangalore, India; Bhat S.R., Department of Commerce, MLA Academy of Higher Learning, Karnataka, Bengaluru, India, School of Commerce and Management, Alliance Ascent College, Bangalore, India; Jagadeesha G.T., Department of Commerce, Seshadripuram College, Karnataka, Tumakuru, India; Manjunatha G., Department of Commerce, St. Claret College, Karnataka, Bengaluru, India; Druvakumar M., Department of Commerce, Kristu Jayanti College, Karnataka, Bengaluru, India
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Title
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Faculty Publications
Article
Faculty Publications -Articles
Dublin Core
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Sharma, Vinod; Kapse, Manohar; Elangovan, N.; Mahajan, Yogesh; Medhini, Bethu
Title
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Analyzing SDGs in high-and-low-emission industries: a comparative study of sustainability reports
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01-01-2025
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Discover Sustainability;Volume;6;Issue;1;Article No.;697;
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<a href="https://doi.org/10.1007/s43621-025-01193-x" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s43621-025-01193-x</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105011686344?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105011686344?origin=resultslist</a>
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Sharma V., Symbiosis Centre for Management and Human Resource Development, Symbiosis International (Deemed University), Pune, India; Kapse M., Jaipuria Institute of Management, Indore, India; Elangovan N., School of Business and Management, Christ University, Bangalore, India; Mahajan Y., Symbiosis Centre for Management and Human Resource Development, Symbiosis International (Deemed University), Pune, India; Medhini B., School of Business and Management, Christ University, Bangalore, India
Description
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This study assesses different Sustainable Development Goals (SDGs) in high- and low-polluting industries through a comparative analysis of sustainability reports. The objective is to evaluate SDG-related terms in reports from 16 companies across four sectorsCement, Automobile, Electric Equipment, and ITover five years. Using Python for data extraction and the text2sdg package in R programming for SDG term detection, the study identifies both prioritized and overlooked SDGs. Results indicate that high and low-polluting industries share similar SDG focus areas. SDGs 6 (Clean Water and Sanitation), 12 (Responsible Consumption and Production), and 13 (Climate Action) received the most attention. In contrast, SDGs 1 (No Poverty), 2 (Zero Hunger), 5 (Gender Equality), 10 (Reduced Inequalities), and 14 (Life Below Water) are consistently underrepresented. The findings suggest that both categories of industries acknowledge the importance of sustainability, yet significant gaps remain in addressing social and environmental challenges. This research contributes to the broader discourse on corporate sustainability and its role in achieving the 2030 Agenda, offering actionable insights for industries to increase their focus on less-considered SDGs. By identifying areas of improvement, the study supports efforts to foster more inclusive and environmentally responsible business practices. The Author(s) 2025.
Subject
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Comparative analysis; High polluting; Low polluting; Sustainability reports; Sustainable development goals; Text analysis
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Springer Nature
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ISSN: 26629984;
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
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online
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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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Analyzing students academic performance using multilayer perceptron model
Subject
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K-means; K-Nearest Neighbor (KNN); Machine Learning; Multilayer Perceptron; Students Performance
Description
An account of the resource
Identification of the students behavior in the class room environment is very important. It helps the lecturer to identify the needs of the students. It also aids in identifying the strength and weakness of the individual and guide them to improve on their performance. Observing and supervising the students regularly can improve their performance. The data has been collected from 120 students who took the common the course taught by two different lectures. The students were observed based on the internal assignments and quizzes and the model exam given by the respective lecturers. In this paper the students are categorized into different groups based on their performance using Multilayer Perceptron (MLP) and also different factors which are influencing the performance of the students are identified. BEIESP.
Creator
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Sinthia G.; Balamurugan M.
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International Journal of Recent Technology and Engineering, Vol-7, No. 5, pp. 156-160.
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Blue Eyes Intelligence Engineering and Sciences Publication
Date
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2019-01-01
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<a href="" target="_blank" rel="noreferrer noopener"></a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068001944&partnerID=40&md5=86e0dc54ef165ac1c593cc4522645ec0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068001944&partnerID=40&md5=86e0dc54ef165ac1c593cc4522645ec0</a>
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ISSN: 22773878
Format
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Online
Language
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English
Type
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Article
Coverage
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Sinthia G., Dept. of Computer Science and Engineering CHRIST (Deemed to be University), Faculty of Engineering, Bangalore, Karnataka, India; Balamurugan M., Dept. of Computer Science and Engineering CHRIST (Deemed to be University), Faculty of Engineering, Bangalore, Karnataka, India
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Title
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Chebbi, Nandan Shrinivas; Mahashabde, Roshan Balaji; Shrinivas, Bharath Masthi; Makam, Prashasth Kishore; Kumar, Sandeep; Kiran, V.
Title
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Analyzing Technology Ecosystem Business Models: A Predictive Modelling Approach
Date
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01-01-2025
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Proceedings of 2025 IEEE International Conference on Contemporary Computing and Communications, InC4 2025;
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<a href="https://doi.org/10.1109/InC465408.2025.11256413" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/InC465408.2025.11256413</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030320346?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030320346?origin=resultslist</a>
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Chebbi N.S., RV College of Engineering, Department of ECE, Bengaluru, India; Mahashabde R.B., RV College of Engineering, Department of ECE, Bengaluru, India; Shrinivas B.M., RV College of Engineering, Department of ECE, Bengaluru, India; Makam P.K., RV College of Engineering, Department of ECE, Bengaluru, India; Kumar S., Christ University, Department of AI, ML and DS, Bengaluru, India; Kiran V., RV College of Engineering, Department of ECE, Bengaluru, India
Description
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In the rapidly changing landscape of technology, companies are devoting an increasing amount of their resources to developing product ecosystems that collaborate to deliver enhanced consumer experiences and strengthen their business models. As opposed to traditional standalone solutions, these ecosystems are intended to facilitate everyday tasks, increase user engagement, and provide seamless integration, all of which ensure a steady stream of revenue and dedicated customer base. This analysis provides an overview of the many ecosystem models that are now transforming the technology industry. An examination of ecosystems that help businesses maintain long-term revenue sustainability and high customer retention rates is provided by the model analysis, along with insights into how ecosystems may enhance user experience by being more connected, straightforward, and user-friendly. Technology ecosystems' quantitative effects are lacking, which makes it difficult to comprehend how they affect long-term revenue sustainability and customer retention. It is challenging to understand how technological ecosystems impact long-term revenue sustainability and customer retention due to the lack of measurable consequences. Through the use of multiple linear regression, this study illustrates the ecosystem business models' long-term revenue and customer retention. The study visualized the relationships of the technology ecosystem with an accuracy of 90-99%. This shows how to measure ecosystem impact and gives firms data-driven insights to improve their ecosystem initiatives. 2025 IEEE.
Subject
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Customer Retention; Ecosystem Business Models; Machine Learning; Predictive Modeling
Publisher
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833152118-9;
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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Faculty Publications
Article
Faculty Publications -Articles
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Nair, Prabhul S.; Tripathi, Abhinav P.
Title
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ANALYZING THE BEHAVIORAL CORRELATES OF GUILT-FREE FOOD CONSUMPTION: STUDY OF GEN Z PERSPECTIVE
Date
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01-01-2025
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Journal of Content, Community and Communication;Volume;22;pp.104-112
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<a href="https://doi.org/10.31620/JCCC.06.25/10" target="_blank" rel="noreferrer noopener">https://doi.org/10.31620/JCCC.06.25/10</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105012902018?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105012902018?origin=resultslist</a>
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Nair P.S., Christ University, Bangalore, India; Tripathi A.P., Christ University, Bangalore, India
Description
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This study investigated the factors influencing Gen Z's purchase intentions for guilt-free food products in India. The aim of the study is to examine the relationship of food habits among the current generation using the Theory of Planned behaviour. Using purposive sampling, the data was collected from 318 Gen Z students in major Indian cities. SPSS and AMOS were utilised to conduct the analysis of the sample. The analysis revealed that attitude, perceived behaviour control and subjective norms significantly influenced the purchase behaviour. The study provides valuable insights for marketers, policymakers, and food producers seeking to promote guilt-free food products among this influential demographic. 2025 Amity University. All rights reserved.
Subject
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Food Neophobia; Gen Z; Guilt-Free Food; Perceived Behavioural Control; Purchase Intention; Subjective Norms
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Amity University
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ISSN: 23957514;
Language
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English
Type
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Article
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All Open Access; Bronze Open Access
Format
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online
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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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Analyzing the Consumer Buying Behavior by Adapting Artificial Intelligence (AI)
Subject
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artificial intelligence (AI); business; consumer; industry 4.0; technology
Description
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In any business consumers or customers are important part of the market, so it is necessary to attract more customers for increasing the profits. The current research in this area has demonstrated that artificial intelligence (AI) has a substantial impact on the end customer, contrary to the widespread notion that it has more of an impact on industry than other manufacturers. There are many studies on the various applications of AI in analyzing and visualizing the consumer behavior. Thus, it is been observed the behavior of consumer is not same for same businesses, it varies from consumer to consumer. In other respects, AI is changing how consumers act right now. In coming year's use of AI will become common where the human dependable businesses also get automated with time. 2024 IEEE.
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Tripathi S.; Tiwari A.; Singh S.; Roy A.; Singla J.; Chaudhry R.
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Proceedings - 2024 International Conference on Healthcare Innovations, Software and Engineering Technologies, HISET 2024, pp. 377-379.
Publisher
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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/HISET61796.2024.00110" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/HISET61796.2024.00110</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211327100&doi=10.1109%2FHISET61796.2024.00110&partnerID=40&md5=bf48747ae59aba47f6b3a8e080d86005" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211327100&doi=10.1109%2fHISET61796.2024.00110&partnerID=40&md5=bf48747ae59aba47f6b3a8e080d86005</a>
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Restricted Access
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ISBN: 979-835036070-7
Format
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Online
Language
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English
Type
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Conference paper
Coverage
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Tripathi S., Christ Deemed to Be University, Delhi, India; Tiwari A., Christ Deemed to Be University, Delhi, India; Singh S., Sharda University, Sharda School of Business Studies, Greater Noida, India; Roy A., University of Lucknow, Institute of Management Sciences, Lucknow, India; Singla J., Himachal Pradesh Kendriya Vishwavidyalya Business School, Himachal Pradesh, India; Chaudhry R., Institute of Management Studies and Research, Rohtak, India
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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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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
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Singh, Yuvika; Shanbhog, Manjula; Singh, Kuljeet
Title
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Analyzing the Diagnosis and Treatment of Astrocytoma, Oligodendroglioma, and Glioblastoma: A Systematic Review
Date
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01-01-2026
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Lecture Notes in Networks and Systems;Volume;1580 LNNS;pp.467-481
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<a href="https://doi.org/10.1007/978-981-95-0629-3_35" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-95-0629-3_35</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105021812468?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105021812468?origin=resultslist</a>
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Singh Y., Department of Computer Science, School of Sciences, Christ University, Delhi, India; Shanbhog M., Department of Computer Science, School of Sciences, Christ University, Delhi, India; Singh K., Department of Computer Science, School of Sciences, Christ University, Delhi, India
Description
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Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has had a substantial impact on a variety of fields, including healthcare, neuro-oncology, and precision medicine. In recent years, the availability of large-scale labeled datasets has allowed AI-driven advances in glioma detection, classification, and prognosis prediction. However, issues remain in assuring model generalizability, interpretability, and real-world clinical application. One of the most significant disadvantages is the underrepresentation of rare glioma subtypes, which prevents appropriate classification and therapy optimization. This study thoroughly assesses AI-based approaches for glioma classification, survival prediction, and biomarker discovery. A comprehensive survey of ML and DL models published between 2015 and 2024 has been conducted, evaluating radiomics-based tumor detection, multi-omics data integration, and AI-assisted decision-making frameworks. The review investigates the usefulness of convolutional neural networks CNNs, support vector machines SVMs, ensemble learning, and hybrid AI architectures, focusing on classification accuracy, sensitivity, and clinical applicability. Despite these advances, AI-driven glioma research faces challenges such as dataset consistency, clinical validation gaps, and a scarcity of explainable AI (XAI) frameworks. This paper offers a comparative analysis of artificial intelligence approaches assessing their strengths, constraints, and clinical relevance in glioma diagnosis and prognosis prediction in order to solve these challenges. The results underscore artificial intelligences revolutionary capacity in redefining glioma diagnosis, enhancing accuracy, and shaping the future of personalized treatment, thereby integrating computational progress with clinical neuro-oncology. Glioma diagnosis, deep learning, astrocytoma, oligodendroglioma, glioblastoma. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
Subject
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Astrocytoma; Brain tumors; Deep learning; Glioblastoma; Glioma diagnosis; Oligodendroglioma
Publisher
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Springer Science and Business Media Deutschland GmbH
Relation
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ISSN: 23673370; ISBN: 978-981950628-6;
Language
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English
Type
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Conference paper
Rights
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Restricted Access; Hardcopy may be available in the library