618
20
16481
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Dublin Core
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Faculty Publications
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Faculty Publications -Articles
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Olubiyi, Timilehin Olasoji; Longe, Olumide Babatope; Susela Devi, K. Suppiah; Sinha, Mudita; ?im?ek, Tu?; ?im?ek, Ahmet Bahad?r
Title
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Prioritisation of Human Resource Strategies in the Digital Transformation Process of SMEs
Date
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01-01-2025
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Contemporary Issues in Entrepreneurship Research;Volume;40;Issue;5b;
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<a href="https://doi.org/10.1108/S2040-724620250000022031" target="_blank" rel="noreferrer noopener">https://doi.org/10.1108/S2040-724620250000022031</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105024787906?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105024787906?origin=resultslist</a>
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Olubiyi T.O., West Midlands Open University, Nigeria; Longe O.B., West Midlands Open University, Nigeria; Susela Devi K.S., Sunway University, Malaysia; Sinha M., Christ University, India; ?im?ek T., Ghane University, Turkey; ?im?ek A.B., Ghane University, Turkey
Description
An account of the resource
This chapter focuses on the importance of human resource (HR) management strategies in the digital business strategy for small- and medium-sized enterprises (SMEs). With the increasing influence of digital transformation that alters organisational structures and implements new technologies, SMEs have no other choice but to evolve. However, due to the scarcity of resources, it becomes very important for SMEs to allocate its HR appropriately and effectively. To support decision-making on strategies like employee reskilling, recruitment of digital talent, leadership development, and promoting a digital culture, this chapter presents a multi-criteria decision analysis (MCDA) framework. The prioritisation is crucial because not all the strategies can be executed at once, and SMEs should target the most effective ones in the long run, affordable, and relevant to their digital transformation agenda. This chapter illustrates methods for how SMEs can use the proposed prioritisation framework effectively. The hypothetical case study demonstrates the real challenges faced by SMEs during digital transformation and how MCDA assists leaders in selecting the most beneficial HR strategies. The case highlights the necessity of fitting strategies to organisational challenges to allow the customisation of training and leadership to align with business demands and maximise effectiveness while minimising costs. Upon use, this framework enables SMEs to comprehend and direct their digital transformation path more effectively. 2026 Tu? ?im?ek and Ahmet Bahad?r ?im?ek
Subject
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AI; Competition; Digital transformation; human resources strategies; L26; M54; multi-criteria decision analysis; O33; Policy; prioritisation; Robots; SME digitalisation; SMEs; Start-ups
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Emerald Publishing
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ISSN: 20407246;
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English
Type
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Article
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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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Roy, Ankita Priti; Anand, Kerena; Elangovan, N.; Halaswamy, D.
Title
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Prioritization of Challenges in EdTech Platform to Enhance User Continuance Intention: A Multi-criteria Decision Making Approach
Date
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01-01-2025
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Lecture Notes in Networks and Systems;Volume;1388 LNNS;pp.377-394
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<a href="https://doi.org/10.1007/978-981-96-6063-6_26" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-6063-6_26</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015652282?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015652282?origin=resultslist</a>
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Roy A.P., School of Business and Management, Christ University, Bengaluru, India; Anand K., School of Business and Management, Christ University, Bengaluru, India; Elangovan N., School of Business and Management, Christ University, Bengaluru, India; Halaswamy D., School of Business and Management, Christ University, Bengaluru, India
Description
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In the rapidly evolving digital education landscape, EdTech platforms face significant challenges that impact user continuance intention. This study employs a fuzzy logic approach within the Multi-criteria Decision Making (MCDM) framework to identify and prioritize these challenges, ensuring the long-term sustainability of EdTech solutions. Key challenges were identified through an extensive literature review and unstructured interviews with eight industry experts. The fuzzy AHP technique was used to rank these challenges, providing a structured approach for EdTech companies to enhance user continuance intention and platform effectiveness. Results reveal Personalization (32.90%) as the most critical factor, followed by Data Privacy and security (20.86%) and User Interface (12.02%). Addressing these prioritized challenges can significantly improve user engagement and contribute to the development of inclusive and accessible educational technologies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Subject
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Challenges; EdTech platform; Fuzzy AHP; MCDM; Prioritization; User continuance intention
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Springer Science and Business Media Deutschland GmbH
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ISSN: 23673370; ISBN: 978-981966062-9;
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English
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Conference paper
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Faculty Publications
Conference Paper
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Srivastava, Shubhi; Gupta, Roopesh; Banerjee, Somnath; Vijayan, Alpha; Thacker, Chintan; Vasmatkar, Abhijit
Title
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Prioritized QoS Enforcement in Smart Healthcare IoT Using Adaptive Deep Q-Network-Based Traffic Decision System
Date
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01-01-2025
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2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2025;
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<a href="https://doi.org/10.1109/IACIS65746.2025.11210968" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IACIS65746.2025.11210968</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105023639181?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105023639181?origin=resultslist</a>
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Srivastava S., New Horizon College of Engineering, Department of Information Science and Engineering, Bengaluru, India; Gupta R., SCES's Indira School of Business Studies, Department of Marketing, Pune, India; Banerjee S., Sun Prairie, United States; Vijayan A., Christ University, Department of Artificial Intelligence Machine Learning & Data Science (AIML&DS), Bengaluru, India; Thacker C., Parul institute of Engineering and Technology, Parul University, Faculty of Engineering and Technology, Department of Computer science and Engineering, P.O.Limda, India; Vasmatkar A., Symbiosis Law School, Symbiosis International (Deemed University), Faculty of Law, Pune, India
Description
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Healthcare IoT systems have been plagued with significant challenges with regard to maintaining an optimum QoS due to the dynamic conditions of the network, diverse device capabilities, and stringent real-time constraints imposed by patient monitoring-type applications. Traditional QoS mechanisms are basically static; they do not take into account changes within the network. Hence, service delivery experiences degradation, with attendant risk to patients' safety. As a solution, this research proposes an adaptive QoS approach employing Deep Q-Network (DQN) reinforcement learning algorithms to dynamically control resource allocation and traffic prioritization in healthcare IoT networks. This system involves multi-agent reinforcement learning architecture where continuous state-action space mapping is utilized for adjusting bandwidth allocation, latency management, and packet prioritization automatically based on network conditions and the criticality levels of applications in real-time. Experimentally, the solution has attained an accuracy of 94.7 percent in QoS prediction, an 87.3 percent reduction in average latency to critical healthcare applications, 91.2 percent improvement in network throughput utilization, and an 89.6 percent success rate in adhering to service level agreements in peak traffic conditions. Through reinforcement learning-based decision making, the adaptive QoS mechanism dynamically accommodates the requirements of healthcare IoT, ensuring reliable service delivery while optimizing the usage of network resources. 2025 IEEE.
Subject
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adaptive systems; healthcare IOT; network optimization; quality of service; reinforcement learning
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833153677-0;
Language
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English
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Conference paper
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Title
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Articles
Article
Faculty Publications -Articles
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Prioritizing evaluation criteria of IoT-driven warehousing startups: asilver lining to the unorganized sector in food supply chain
Subject
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AHP and COPRAS; Food and beverage; Internet of Things; Unorganized sector; Warehousing startups
Description
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Purpose: This research is designed to meet two research objectives: firstly, to weigh up the criteria of Internet of Things (IoT) adoption in warehousing startups; secondly, to rank warehousing startups on the basis of benefits they derive from IoT adoption catering to an unorganized sector in the food supply chain. Design/methodology/approach: A blend of analytic hierarchy process (AHP) and complex proportional assessment (COPRAS) methods of multi-criteria decision-making techniques were applied. AHP determined the weights of various criteria using pairwise comparison, and COPRAS technique ranked the 10 warehousing startups on account of performance indicators. The study has been conducted at the warehousing startups of Bangalore, a hub of food warehousing startups. Findings: The critical findings of the study revealed that these food warehouse startups attain improved productivity in terms of enhancing efficiency when implemented with IoT adoption. When evaluated using both AHP and COPRAS techniques, the combined results show WH5 as the best performing and WH10 as the least performing warehouse startups. Practical implications: Warehouses that are embarking on their business opportunity in food storage can strategize to leverage the benefits of IoT in terms of food safety and security, capacity planning, layout design, space utilization and resilience. Originality/value: Despite the numerous research works on food supply chain, the research on IoT in warehousing startups is limited. The rankings for the 10 food warehousing startups integrated with IoT using AHP-COPRAS approaches are the novelty of this work. 2024, Emerald Publishing Limited.
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Vedapradha R.; Joshi D.; Hariharan R.
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International Journal of Logistics Management
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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/IJLM-12-2023-0509" target="_blank" rel="noreferrer noopener">https://doi.org/10.1108/IJLM-12-2023-0509</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195580377&doi=10.1108%2FIJLM-12-2023-0509&partnerID=40&md5=287b586104bff54a5959c2c04451c726" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195580377&doi=10.1108%2fIJLM-12-2023-0509&partnerID=40&md5=287b586104bff54a5959c2c04451c726</a>
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ISSN: 9574093
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Online
Language
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English
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Article
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Vedapradha R., School of Commerce, Mount Carmel College Autonomous, Bangalore, India; Joshi D., Department of Operations and Supply Chain Management, St. Josephs Institute of Management, Bangalore, India; Hariharan R., School of Business and Management, Christ (Deemed to be University), Yeshwanthpur Campus, Bengaluru, India
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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/.
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Faculty Publications
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Vedapradha, R.; Joshi, Deepika; Hariharan, R.
Title
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Prioritizing evaluation criteria of IoT-driven warehousing startups: asilver lining to the unorganized sector in food supply chain
Date
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01-01-2025
Source
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International Journal of Logistics Management;Volume;36;Issue;2;pp.589-610
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<a href="https://doi.org/10.1108/IJLM-12-2023-0509" target="_blank" rel="noreferrer noopener">https://doi.org/10.1108/IJLM-12-2023-0509</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105001083015?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105001083015?origin=resultslist</a>
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Vedapradha R., School of Commerce, Mount Carmel College Autonomous, Bangalore, India; Joshi D., Department of Operations and Supply Chain Management, St. Josephs Institute of Management, Bangalore, India; Hariharan R., School of Business and Management, Christ (Deemed to be University), Yeshwanthpur Campus, Bengaluru, India
Description
An account of the resource
Purpose: This research is designed to meet two research objectives: firstly, to weigh up the criteria of Internet of Things (IoT) adoption in warehousing startups; secondly, to rank warehousing startups on the basis of benefits they derive from IoT adoption catering to an unorganized sector in the food supply chain. Design/methodology/approach: A blend of analytic hierarchy process (AHP) and complex proportional assessment (COPRAS) methods of multi-criteria decision-making techniques were applied. AHP determined the weights of various criteria using pairwise comparison, and COPRAS technique ranked the 10 warehousing startups on account of performance indicators. The study has been conducted at the warehousing startups of Bangalore, a hub of food warehousing startups. Findings: The critical findings of the study revealed that these food warehouse startups attain improved productivity in terms of enhancing efficiency when implemented with IoT adoption. When evaluated using both AHP and COPRAS techniques, the combined results show WH5 as the best performing and WH10 as the least performing warehouse startups. Practical implications: Warehouses that are embarking on their business opportunity in food storage can strategize to leverage the benefits of IoT in terms of food safety and security, capacity planning, layout design, space utilization and resilience. Originality/value: Despite the numerous research works on food supply chain, the research on IoT in warehousing startups is limited. The rankings for the 10 food warehousing startups integrated with IoT using AHP-COPRAS approaches are the novelty of this work. 2024, Emerald Publishing Limited.
Subject
The topic of the resource
AHP and COPRAS; Food and beverage; Internet of Things; Unorganized sector; Warehousing startups
Publisher
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Emerald Publishing
Relation
A related resource
ISSN: 9574093;
Language
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English
Type
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Article
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online
-
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
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Prioritizing Factors Affecting Customers Satisfaction in the Internet Banking Using Artificial Intelligence
Subject
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Artificial Intelligence; Convolutional Neural Networks; Customer Satisfaction; etc; Internet Banking; Random Forest
Description
An account of the resource
Internet banking has revolutionised the way customers interact with their banks, providing them with convenient access to a wide range of financial services from the comfort of their homes or mobile devices. Customer satisfaction the success of an endeavour is contingent upon a vital component internet banking Service provision, as it pertains directly impacts customer retention and loyalty. This research explores the application of artificial intelligence (AI) techniques, specifically random forest and convolutional neural networks (CNN), to prioritise the factors that affect customer satisfaction in internet banking. The study begins with data collection from a diverse sample of internet banking customers, including demographic information, transaction history, and customer feedback. These may include the ease of navigation, the response time of the platform, and the level of trust in the bank's security measures. Furthermore, convolutional neural networks (CNN) are utilised to analyse unstructured data such as customer feedback and reviews. By applying natural language processing techniques, CNN s extract sentiment and topic information from customer comments. This approach can ultimately lead to improved customer retention and loyalty, ensuring the long-term success and competitiveness of internet banking platforms. In conclusion, this study showcases the power of AI, specifically Random Forest and CNN, in prioritising factors affecting customer satisfaction in internet banking. It highlights the significance of using both quantitative and qualitative investigations in order to attain a comprehensive comprehension of customer sentiments and preferences in the digital banking landscape. 2024 IEEE.
Creator
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Soundarraj P.L.; Javaraj P.; Meetna S.; Elaiyaraja A.; Naqvi S.R.; Gamadia R.
Source
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Proceedings of 9th International Conference on Science, Technology, Engineering and Mathematics: The Role of Emerging Technologies in Digital Transformation, ICONSTEM 2024
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/ICONSTEM60960.2024.10568595" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICONSTEM60960.2024.10568595</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198853225&doi=10.1109%2FICONSTEM60960.2024.10568595&partnerID=40&md5=cc0330807822ac68b00fb053dcb616e0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198853225&doi=10.1109%2fICONSTEM60960.2024.10568595&partnerID=40&md5=cc0330807822ac68b00fb053dcb616e0</a>
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Restricted Access
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ISBN: 979-835036509-2
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Online
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English
Type
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Conference paper
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Soundarraj P.L., School of Business And Management, Christ University, Maharashtra, Pune, 412112, India; Javaraj P., Sri Ramakrishna College of Arts And Science, Department of B. Com It, Coimbatore, India; Meetna S., Amrita School of Business, Coimbatore, India; Elaiyaraja A., PanimalarEngineering College, Department of Management Studies, Chennai, India; Naqvi S.R., Amity Business School, Amity University, Department of Human Resource And Organizational Behaviour, Up, Noida, India; Gamadia R., C K Shah Vijapurwala Institute of Management, Department of Management, Gujarat, Vadodara, 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/.
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Faculty Publications
Conference Paper
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Dublin Core
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Roy, Ankita Priti; Anand, Kerena; Elangovan, N.; Halaswamy, D.
Title
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Prioritizing Risks in AI-Enabled EdTech Platforms: An Analytic Hierarchy Process Approach
Date
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01-01-2025
Source
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Lecture Notes in Networks and Systems;Volume;5588 LNNS;pp.549-564
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<a href="https://doi.org/10.1007/978-981-96-1918-4_39" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-1918-4_39</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105009982799?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105009982799?origin=resultslist</a>
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Roy A.P., School of Business and Management, Christ University, Bengaluru, India; Anand K., School of Business and Management, Christ University, Bengaluru, India; Elangovan N., School of Business and Management, Christ University, Bengaluru, India; Halaswamy D., School of Business and Management, Christ University, Bengaluru, India
Description
An account of the resource
Artificial intelligence (AI) has revolutionized educational technology (EdTech) platforms, offering innovative tools and personalized learning experiences. However, the integration of AI into EdTech also introduces significant risks that must be addressed. This study aims to systematically prioritize and evaluate these risks using the Analytic Hierarchy Process (AHP), a structured multi-criteria decision-making tool. Key risks identified include data privacy and security concerns, algorithmic bias and fairness issues, technical reliability and compatibility challenges, personalization and overreliance on AI, accessibility and equity risks, ethical and privacy concerns, operational and financial risks, governance and regulatory compliance risks, and the black box problem of lack of transparency in AI decision-making processes. Through an extensive literature review and expert inputs, the study employs the AHP methodology to capture the diverse dimensions of risk and determine their relative importance. The findings highlight technical reliability and compatibility (27.43%), the black box problem (17.31%), ethical concerns (17.29%), and personalization and overreliance on AI (17.25%) as the top-ranked risks. By identifying critical risk areas, this research informs the establishment of effective risk management strategies, ensuring the responsible and sustainable adoption of AI in EdTech platforms. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Subject
The topic of the resource
Analytic Hierarchy Process (AHP); Artificial Intelligence (AI) EdTech platform; Multi-criteria decision-making (MCDM); Risk prioritization
Publisher
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Springer Science and Business Media Deutschland GmbH
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A related resource
ISSN: 23673370; ISBN: 978-981961917-7;
Language
A language of the resource
English
Type
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Conference paper
Rights
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Restricted Access; Hardcopy may be available in the library
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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/.
Creator
An entity primarily responsible for making the resource
Roy, Ankita Priti; Anand, Kerena; Elangovan, N.; Halaswamy, D.
Title
A name given to the resource
Prioritizing Risks in AI-Enabled EdTech Platforms: An Analytic Hierarchy Process Approach
Date
A point or period of time associated with an event in the lifecycle of the resource
01-01-2025
Source
A related resource from which the described resource is derived
Lecture Notes in Networks and Systems;Volume;5588 LNNS;pp.549-564
Identifier
An unambiguous reference to the resource within a given context
<a href="https://doi.org/10.1007/978-981-96-1918-4_39" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-1918-4_39</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105009982799?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105009982799?origin=resultslist</a>
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Roy A.P., School of Business and Management, Christ University, Bengaluru, India; Anand K., School of Business and Management, Christ University, Bengaluru, India; Elangovan N., School of Business and Management, Christ University, Bengaluru, India; Halaswamy D., School of Business and Management, Christ University, Bengaluru, India
Description
An account of the resource
Artificial intelligence (AI) has revolutionized educational technology (EdTech) platforms, offering innovative tools and personalized learning experiences. However, the integration of AI into EdTech also introduces significant risks that must be addressed. This study aims to systematically prioritize and evaluate these risks using the Analytic Hierarchy Process (AHP), a structured multi-criteria decision-making tool. Key risks identified include data privacy and security concerns, algorithmic bias and fairness issues, technical reliability and compatibility challenges, personalization and overreliance on AI, accessibility and equity risks, ethical and privacy concerns, operational and financial risks, governance and regulatory compliance risks, and the black box problem of lack of transparency in AI decision-making processes. Through an extensive literature review and expert inputs, the study employs the AHP methodology to capture the diverse dimensions of risk and determine their relative importance. The findings highlight technical reliability and compatibility (27.43%), the black box problem (17.31%), ethical concerns (17.29%), and personalization and overreliance on AI (17.25%) as the top-ranked risks. By identifying critical risk areas, this research informs the establishment of effective risk management strategies, ensuring the responsible and sustainable adoption of AI in EdTech platforms. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Subject
The topic of the resource
Analytic Hierarchy Process (AHP); Artificial Intelligence (AI) EdTech platform; Multi-criteria decision-making (MCDM); Risk prioritization
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Springer Science and Business Media Deutschland GmbH
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ISSN: 23673370; ISBN: 978-981961917-7;
Language
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English
Type
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Conference paper
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Roy, Ankita Priti; Anand, Kerena; Halaswamy, D.; Elangovan, N.
Title
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Prioritizing Risks in IoT-Enabled EdTech Platforms: A Fuzzy AHP Approach to Maximize User Satisfaction
Date
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01-01-2026
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Lecture Notes in Networks and Systems;Volume;1445 LNNS;pp.29-45
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<a href="https://doi.org/10.1007/978-981-96-7140-3_3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-7140-3_3</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028290225?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028290225?origin=resultslist</a>
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Roy A.P., School of Business and Management, Christ University, Bengaluru, India; Anand K., School of Business and Management, Christ University, Bengaluru, India; Halaswamy D., School of Business and Management, Christ University, Bengaluru, India; Elangovan N., School of Business and Management, Christ University, Bengaluru, India
Description
An account of the resource
The integration of the Internet of Things (IoT) in educational technology (EdTech) platforms offers personalized, adaptive learning but introduces significant risks. This study identifies and prioritizes these risks using the Analytic Hierarchy Process (AHP) and fuzzy AHP techniques. Seven industry experts provided input, complemented by a comprehensive literature review. The analysis reveals ethical considerations (30.17%) and data privacy/security (29.22%) as top concerns, followed by regulatory compliance (12.91%), high implementation costs (10.99%), and technical expertise requirements (8.37%). Surprisingly, scalability concerns (1.70%) and data accuracy/reliability (2.63%) rank lower. These findings emphasize the need for a human-centric approach in IoT-enhanced EdTech deployment, focusing on responsible implementation and regulatory adherence. The study provides valuable insights for EdTech companies and educational institutions, guiding strategic decision-making to enhance user satisfaction and ensure sustainable development of IoT-enhanced educational platforms. Future research could explore more advanced mathematical models and context-specific challenges to refine risk prioritization strategies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
Subject
The topic of the resource
Fuzzy analytic hierarchy process (AHP); Internet-of-things (IoT) EdTech platform; Multi-criteria decision-making (MCDM); Risk prioritization
Publisher
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Springer Science and Business Media Deutschland GmbH
Relation
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ISSN: 23673370; ISBN: 978-981967139-7;
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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Articles
Article
Faculty Publications -Articles
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Title
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Prioritizing the Essentials: The MBA Aspirants Dilemma
Subject
The topic of the resource
Chi-Square; Correlation; Exploratory Data Analysis; One-way ANOVA; Regression and Random Forest; t-test; Welch Test
Description
An account of the resource
Objective decision-making while choosing an appropriate college for a Master in Business Administration (MBA) is only half-done. It is critical that the student be able to find the best placement at the end of the course by acquiring the most critical skills/specializations affecting placements and involves data-driven decision-making based on past placement trends. Viti and Vania have done their preliminary selection, of ABC College for their MBA course, based on the colleges credence quality. However, they are trying to understand the key success factors (KSFs) affecting placements at ABC to focus their next two years on getting most placement-ready. Having been provided with the placement details of the outgoing batch, they are looking to analyze the data to discover the most critical parameters affecting placements. NeilsonJournals Publishing 2023.
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Sharma V.; Poulose J.; Kapse M.; Sanjeev M.A.; Mahajan Y.
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Journal of Organizational Behavior Education, Vol-16, pp. 121-126.
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NeilsonJournals Publishing
Date
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2023-01-01
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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-s2.0-85193909851&partnerID=40&md5=922a8633ae7a072e64d0dc046406901c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193909851&partnerID=40&md5=922a8633ae7a072e64d0dc046406901c</a>
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ISSN: 16497627
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Online
Language
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English
Type
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Article
Coverage
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Sharma V., Symbiosis Centre for Management and Human Resource Development (SCMHRD), Symbiosis International (Deemed University), Pune, India; Poulose J., School of Business and Management, CHRIST (Deemed to be University) Delhi-NCR, India; Kapse M., Symbiosis Centre for Management and Human Resource Development (SCMHRD), Symbiosis International (Deemed University), Pune, India; Sanjeev M.A., Symbiosis Centre for Management and Human Resource Development (SCMHRD), Symbiosis International (Deemed University), Pune, India; Mahajan Y., Symbiosis Centre for Management and Human Resource Development (SCMHRD), Symbiosis International (Deemed University), Pune, India
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Title
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Articles
Article
Faculty Publications -Articles
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Priority based prediction mechanism for ranking providers in federated cloud architecture
Subject
The topic of the resource
Baysian network; Broker manager; Cloud broker; Grade values; Priority feedback decision tree
Description
An account of the resource
Cloud computing is a growing and excellent technology, as exponentially increasing the interest among users to utilize cloud applications; they need to depend on any one of the particular service provider. Now a days number of service providers also rapidly increasing in wide range, this leads ambiguity and distrust among the users. In this paper, enhanced broker based federated cloud architecture is proposed to resolve the selection of service provider issue using grading techniques and results proved that better performance improvement than single service provider selection. This broker architecture also addresses to selects the appropriate service provider automatically in the federated cloud architecture for the users submitted requests by previous experience with help of Bayesian network model. The former one implemented through concept of grade system. It is constructed for categorizing the providers based on the level of available resources. Grade and grade values distributed by applying the grade distribution algorithm for distinguishes the components. Total grade values computed for every service provider and sorted using quick sort algorithm to grade the cloud service providers. Priority based feedback decision tree technique added with this for separates similar grade cloud service provider in the selected list. Second Bayesian network model also used to rank the cloud service providers according to the previous performance of the providers with customers. Probability of satisfied customers feedback calculated for individual Service Measurements Index of Cloud Service Providers. 2018, Springer Science+Business Media, LLC, part of Springer Nature.
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Saravanan M.; Aramudhan M.; SundaraPandiyan S.; Avudaiappan T.
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Cluster Computing, Vol-22, pp. 9815-9823.
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Springer
Date
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2019-01-01
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<a href="https://doi.org/10.1007/s10586-017-1593-x" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10586-017-1593-x</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040033548&doi=10.1007%2Fs10586-017-1593-x&partnerID=40&md5=7a55732207db70cb69f672e324918658" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040033548&doi=10.1007%2fs10586-017-1593-x&partnerID=40&md5=7a55732207db70cb69f672e324918658</a>
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ISSN: 13867857
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Online
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English
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Article
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Saravanan M., SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India; Aramudhan M., Perunthalaivar Kamarajar College of Engineering and Tech, Karaikkal, India; SundaraPandiyan S., Christ (Deemed to be University), Bengaluru, Karnataka, India; Avudaiappan T., K. Ramakrishnan College of Technology, Trichy, Tamilnadu, India
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Title
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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Priority-driven Unbalanced Transportation Problem (PUTP) to obtain better Initial Feasible Solution
Subject
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PPUTP; Priority-driven Unbalanced Transportation Problem; VAM
Description
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In this paper, we tackle the Priority-driven Unbalanced Transportation Problem (PUTP), a scenario where total demand exceeds total supply. An innovative algorithm, the Penalty-driven Priority-driven Unbalanced Transportation Problem (PPUTP) is introduced to solve this challenge. PPUTP allocates supplies to high-priority demands by computing penalties and sequentially addressing the most penalized demands, thereby ensuring priority demands are met efficiently. A comparative analysis with Vogel's Approximation Method (VAM) across various problem sets ranging from 5x5 to 50x50 dimensions demonstrates the efficiency of our algorithms. PPUTP consistently shows lower percentage increments from the optimal solution, indicating its robustness in providing near-optimal solutions. This study highlights the importance of algorithm selection based on problem set dimensions and complexity in Priority-driven Unbalanced Transportation Problem, with PPUTP emerging as a versatile and robust solution across various scenarios. 2024 IEEE.
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Arif A.S.; Babu M.A.; Khan A.R.; Islam M.N.; Uddin M.S.; Poonia R.C.
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Proceedings of InC4 2024 - 2024 IEEE International Conference on Contemporary Computing and Communications
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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/InC460750.2024.10649090" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/InC460750.2024.10649090</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203814159&doi=10.1109%2FInC460750.2024.10649090&partnerID=40&md5=e630a697e3100f13011f46cbdcc94201" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203814159&doi=10.1109%2fInC460750.2024.10649090&partnerID=40&md5=e630a697e3100f13011f46cbdcc94201</a>
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ISBN: 979-835038365-2
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Online
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English
Type
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Conference paper
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Arif A.S., Jahangirnagar University, Department of Mathematics, Dhaka, Savar, Bangladesh; Babu M.A., Independent University Bangladesh, Department of Physical Sciences, Dhaka, 1229, Bangladesh; Khan A.R., Jahangirnagar University, Department of Mathematics, Dhaka, Savar, Bangladesh; Islam M.N., Prince Sattam bin Abdulaziz University, Department of Industrial Engineering, Al Kharj, 11942, Saudi Arabia; Uddin M.S., CHRIST (Deemed to be University), Department of Computer Science, Delhi NCR, 201003, India; Poonia R.C., CHRIST (Deemed to be University), Department of Computer Science, Delhi NCR, 201003, India
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https://archives.christuniversity.in/files/original/9fdc3ee197aebb04df85c9f911e8929f.pdf
85114c321af30b4930c6b5fd43b7f927
Dublin Core
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Title
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MPHIL
Mphil
Mphil Thesis
Dublin Core
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Title
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Privacy and Data Protection-
A Study of Current Indian Legal Framework
Creator
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Thomas Sarah
Date
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2012
Source
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Law
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Dublin Core
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Title
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Articles
Article
Faculty Publications -Articles
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Title
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Privacy breach perceptions and litigation intentions: Evidence from e-commerce customers
Subject
The topic of the resource
Complexity; Litigation; Litigation intentions; Privacy breach; Privacy control beliefs; Self efficacy
Description
An account of the resource
This paper examines the formation of litigation intentions among e-commerce customers under the privacy breach due to the influence of antecedents such as vulnerability, social risk, privacy dispositions, effectiveness privacy policy, awareness of data management and moderators such as privacy control beliefs, efficacy in coping and litigation complexity. A structural equation modelling analysis revealed that reasons for privacy breach perceptions are customer dispositions about privacy, anticipated vulnerability due to privacy breach, and social risk related to personal information disclosure. The control beliefs and coping skills of customers under privacy threat positively moderate litigation intentions. Similarly, the task complexity of litigation significantly reduces litigation intentions. 2021
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G R.; V G.; Dinesh K.G.S.; Asokan-Ajitha A.; Jose A.
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IIMB Management Review, Vol-33, No. 4, pp. 322-336.
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Elsevier Ltd
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2021-01-01
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<a href="https://doi.org/10.1016/j.iimb.2021.11.001" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.iimb.2021.11.001</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122372942&doi=10.1016%2Fj.iimb.2021.11.001&partnerID=40&md5=49ff675f4f798f651be68c41a28a7c87" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122372942&doi=10.1016%2fj.iimb.2021.11.001&partnerID=40&md5=49ff675f4f798f651be68c41a28a7c87</a>
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All Open Access; Gold Open Access
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ISSN: 9703896
Format
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Online
Language
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English
Type
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Article
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G R., Amrita School of Business, Amrita Vishwa Vidyapeetham, Kerala, Kochi, India; V G., Amrita School of Business, Amrita Vishwa Vidyapeetham, Kerala, Kochi, India; Dinesh K.G.S., Amrita School of Business, Amrita Vishwa Vidyapeetham, Kerala, Kochi, India; Asokan-Ajitha A., Indian Institute of Management Amritsar, Punjab, Amritsar, India; Jose A., Department of Professional Studies, 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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Privacy Optimization in Sensors Based Networks With Industrial Processes Management
Subject
The topic of the resource
Cybersecurity; Denial-of-Service (DDoS); Industrial Process Management; Integrated Security Framework; Internet of Things (IoT); Process Management
Description
An account of the resource
The Internet of Things (IoT) also known as IoT has the potential that is required to revolutionize industries, this has been discussed in this research article. Advancements in technology have made devices affordable, efficient and reliable. Different sectors have already started to incorporate these devices into their operations to boost productivity, to minimize failure and downtime. They also use it to optimize resource utilization which is also an important factor. However, the use of these devices also has some security challenges which need to be handled. This research paper proposes a security model specifically designed for process management in the industries. The goal of this model is to find the vulnerabilities, to minimize the risks and threats. Also ensuring integrity, confidentiality and availability of processes is a part of the goal. This paper gives evidence from its implementation and trial apart from its explanation. During the implementation phase, the sensitive data achieved a 100% encryption rate, for protection. Also, integrity checks were conducted on 99.8% of data to guarantee data integrity. 2023 IEEE.
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Siddiqui A.F.; Mishra S.; Jena M.M.; Sharma V.; Al-Khasawneh M.A.; Alkhayyat A.
Source
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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Date
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2023-01-01
Identifier
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<a href="https://doi.org/10.1109/ICTBIG59752.2023.10455985" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG59752.2023.10455985</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189239393&doi=10.1109%2FICTBIG59752.2023.10455985&partnerID=40&md5=2bfaa41e893062c3cfe6000d5b7fe145" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189239393&doi=10.1109%2fICTBIG59752.2023.10455985&partnerID=40&md5=2bfaa41e893062c3cfe6000d5b7fe145</a>
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ISBN: 979-835034327-4
Format
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Online
Language
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English
Type
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Conference paper
Coverage
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Siddiqui A.F., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Mishra S., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Jena M.M., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Sharma V., Christ (Deemed to Be University), Computer Science Department, Delhi NCR, India; Al-Khasawneh M.A., School of Computing, Skyline University College, University City Sharjah, United Arab Emirates; Alkhayyat A., College of Technical Engineering, The Islamic University, Najaf, Iraq
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Faculty Publications
Article
Faculty Publications -Articles
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Banerjee, Jayanta; Kiran, Prabha; Kishore, S.V. Krishna; Vasudevan, M.
Title
A name given to the resource
Privacy over instant messaging platforms: are users making the right decisions?
Date
A point or period of time associated with an event in the lifecycle of the resource
01-01-2025
Source
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International Journal of Public Sector Performance Management;Volume;16;Issue;3;pp.367-385
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<a href="https://doi.org/10.1504/IJPSPM.2025.148526" target="_blank" rel="noreferrer noopener">https://doi.org/10.1504/IJPSPM.2025.148526</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105016123163?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105016123163?origin=resultslist</a>
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Banerjee J., College of Business, City University Ajman, United Arab Emirates; Kiran P., Westminster International University in Tashkent, 12 Istikbol Street, Tashkent, 100047, Uzbekistan; Kishore S.V.K., Christ University, Hosur Road, Near Diary Circle Bangalore, Karnataka, 560029, India; Vasudevan M., Christ University, Hosur Road, Near Diary Circle Bangalore, Karnataka, 560029, India
Description
An account of the resource
This article explores the impact of perceived vulnerability, self-efficacy, resistance to change, and habit on users perception of privacy over users intention to use messaging platforms. The conceptual model includes perceived vulnerability, self-efficacy, resistance to change, habit, and its impact on users perception of privacy over users intention to use messaging platforms. A structural equation and hierarchical regression model were used for data analysis. The results show that age and profession affect peoples decision of shifting to a different platform significantly. The study is based on a few specific instant messaging platforms at one particular point in time and is undertaken in India; hence, the findings cannot be extended/applicable to other countries. The paper discusses the factors impacting the users sensitivity to data privacy while using a communication application through an electronic device, especially a mobile phone. Copyright 2025 Inderscience Enterprises Ltd.
Subject
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data privacy; information privacy; instant messaging platform; privacy concern
Publisher
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Inderscience Publishers
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ISSN: 17411041;
Language
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English
Type
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Article
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Restricted Access; Hardcopy may be available in the library
Format
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online
-
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Abraham, Anina; Hiremath, Manjunatha; Pohrmen, Fabiola Hazel
Title
A name given to the resource
Privacy Preserving Authentication Using Zero Knowledge Proofs
Date
A point or period of time associated with an event in the lifecycle of the resource
01-01-2025
Source
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2025 17th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2025;pp.1003-1008
Identifier
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<a href="https://doi.org/10.1109/CICN67655.2025.11368139" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CICN67655.2025.11368139</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034673715?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034673715?origin=resultslist</a>
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Abraham A., Christ University, Bengaluru, India; Hiremath M., Christ University, Bengaluru, India; Pohrmen F.H., Christ University, Bengaluru, India
Description
An account of the resource
Conventional authentication techniques, such as one-time passwords and passwords, are extremely susceptible to data breaches, credential theft, and phishing attacks. These vulnerabilities are increased when using shared or public devices. This paper proposes a password-less authentication architecture for various environments and organization based on Zero-Knowledge Proofs in order to overcome these issues. The proposed model ensures that no sensitive credentials are sent or retained by having a user demonstrate that they possess a secret without disclosing it to the server. In doing so, the attack surface linked to traditional login methods is greatly reduced. The framework is meant to be scalable, lightweight and easy to integrate with learning management systems, corporate sites, online test platforms, and university websites. 2025 IEEE.
Subject
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Cybersecurity; Privacy-Preserving Authentication; Schnorr protocol; z k-SNARKs; z k-STARKs; Zero-Knowledge Proof (ZKP)
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Institute of Electrical and Electronics Engineers Inc.
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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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Faculty Publications
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Faculty Publications- Conference Papers
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Pranav, R.; Rajoriya, Meenakshi Malviya; Rohini, V.
Title
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Privacy Risk Prediction from Social Media Metadata using Feature Selection Approaches
Date
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01-01-2025
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International Conference on NexGen Networks and Cybernetics, IC2NC 2025 - Proceedings;pp.61-66
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<a href="https://doi.org/10.1109/IC2NC67409.2025.11376087" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC2NC67409.2025.11376087</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035587977?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035587977?origin=resultslist</a>
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Pranav R., Christ University, Department of Computer Science, Bengaluru, India; Rajoriya M.M., Christ University, Department of Computer Science, Bengaluru, India; Rohini V., Christ University, Department of Computer Science, Bengaluru, India
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Millions of new people sign up to online social networks (OSNs) every year, which contributes to the growing spread of Personally Identifiable Information. This often ends up occurring unconsciously, either due to the low stakes involved or because the user doesn't understand or underestimates what can go wrong. This trend indicates the need for a trustworthy means to quantify the privacy danger of sharing information online. The volume of OSN data can simply be too staggering for any degree of meaningful manual review, given both the time and man-hours this would entail. This research presents a two-step, unsupervised, and efficient method to estimate privacy risks at the post level. The first step involves using the most advanced reasoning-based Large Language Model, Gemini 2.5 Pro, to generate a comprehensive 'vulnerability score', which is used as a reference for model training. The next step involves comparing the two most used machine learning feature selection techniques, Recursive Feature Elimination (RFE) and Correlation-Based Selection, to select the best features for predicting this score from metadata alone. The results indicate that Correlation-Based Selection produces better results for both the regression and classification-based models, and the top-performing regression model achieves an R-squared of 0.86. Through this, a practical and scalable method to identify privacy-sensitive content effectively on large datasets has been presented in this study. 2025 IEEE.
Subject
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Feature Selection; Large Language Models; Online Social Networks; Privacy Risk; PrivacySensitive Content
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Relation
A related resource
ISBN: 979-833159484-8;
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
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Dublin Core
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Title
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Pasha, Fairoz; Natarajan, Jayapandian
Title
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Privacy-Integrity Aware Efficient Workflow Scheduler for Edge-Cloud Platform
Date
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01-01-2025
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Lecture Notes in Networks and Systems;Volume;1305 LNNS;pp.171-184
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<a href="https://doi.org/10.1007/978-981-96-3725-6_15" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-3725-6_15</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105011946503?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105011946503?origin=resultslist</a>
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Pasha F., Department of Computer Science and Engineering, Christ University, Bangalore, India; Natarajan J., Department of Computer Science and Engineering, Christ University, Bangalore, India
Description
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Scientific workflow execution in edge-cloud platform have attained in-creased attention, due to reduction in overall makespan assuring workflow deadlines. The workflow task is composed of diverse subtasks which are either executed in edge and the cloud; they are prone to security risk. Any loss of security breach will result to privacy and data integrity issues. Thus, providing security and meeting workflow execution strict deadlines becomes extremely difficult. The current workflow scheduling methods failed to assure both privacy and integrity together under edge-cloud computing platform. In addressing the research security and efficiency issues, this article introduced a novel approach namely Privacy-Integrity Aware Efficient Workflow Scheduler (PIAEWS) for edge-cloud platform. The PIAEWS introduces a novel trust metrics to assure only authenticated node takes part communication and consensus model to assure data integrity without compromising on user privacy constraint. The PIAEWS improves makespan and reduces overall energy consumption by assuring both security and performance together when executing genome sequencing workflow application in edge-cloud platform. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Subject
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Cloud computing; Edge-server; Integrity; Privacy; Scheduling; Scientific workflow; Security
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Springer Science and Business Media Deutschland GmbH
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A related resource
ISSN: 23673370; ISBN: 978-981963724-9;
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
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Title
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Faculty Publications
Article
Faculty Publications -Articles
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Namitha, T.N.; Raghavendra, S.; Vinith, R.
Title
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Privacy-Preserving Federated Learning for Prognostic Modeling in Rare Diseases: A Scalable Case Study on Kawasaki Disease
Date
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01-01-2025
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International Journal of Online and Biomedical Engineering;Volume;21;Issue;11;pp.66-80
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<a href="https://doi.org/10.3991/ijoe.v21i11.56385" target="_blank" rel="noreferrer noopener">https://doi.org/10.3991/ijoe.v21i11.56385</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105017085885?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105017085885?origin=resultslist</a>
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Namitha T.N., Department of CSE, Christ (Deemed to be University), Karnataka, Bangalore, India; Raghavendra S., Department of AIML and Data Science, Christ (Deemed to be University), Karnataka, Bangalore, India; Vinith R., Amrita Vishwa Vidyapeetham, Tamil Nadu, Coimbatore, India
Description
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Predictive modeling in rare diseases faces major challenges, including data scarcity, class imbalance, and strict privacy regulations that limit cross-border collaboration. These challenges are particularly critical in Kawasaki disease (KD)a rare vasculitis in childrenwhere 10% to 20% of patients are resistant to intravenous immunoglobulin (IVIG), the standard first-line treatment. This significantly increases the risk of coronary artery abnormalities (CAA), making early and accurate prediction of resistance to IVIG essential for improving patient outcomes. Our work proposes a federated learning (FL) approach to address the constraints imposed by security and privacy concerns. We investigate convolutional neural networks (CNN) as the shared model, collaboratively trained across clients. Coupled with strategies to address class imbalance resulting from the rarity of the condition, the federated approach yielded promising results when evaluated against conventional machine learning (ML) models. The proposed approach demonstrated strong performance, achieving 94% accuracy, 93% precision, 89% recall, and 91% F1 score. To ensure robustness and generalizability, an independent dataset was also used, where the proposed model excelled similarly. These results highlight the potential of FL to overcome data privacy barriers and provide a scalable, secure solution for predictive modeling in rare diseases, supporting its integration into medical prediction workflows. 2025 by the authors of this article.
Subject
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adaptive synthetic sampling; convolutional neural network (CNN); federated learning (FL); flower framework; intravenous immunoglobulin resistance; Kawasaki disease (KD); rare disease
Publisher
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International Federation of Engineering Education Societies (IFEES)
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ISSN: 26268493;
Language
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English
Type
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Article
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All Open Access; Gold Open Access
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online