134
20
16481
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https://archives.christuniversity.in/files/original/b9fc3f6d4dc13afbc3d04e29ba7fcda4.jpg
0b6dfeeadbd8dbf97f5aef738454f6b9
Dublin Core
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Title
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Patents
Subject
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Patent
Description
An account of the resource
Patent
Patent
Patents
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Title
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Artificial intelligence based system and method for management, recommendation, mapping of skill /
Subject
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Computer Science
Description
An account of the resource
Patent Number: 202111054501, Applicant: Durgansh Sharma.
Artificial intelligence based system (100) and method for management, recommendation, mapping of skill comprising EmpNet (101), recommender system (102), automated machine learning system (103), skillset dataset (104), optimization system (105), industry interface system (106). The method for management, recommendation, mapping of skill comprising the steps of: a) capturing the required skillset personal data by the panchayat system (701); b) verify the skillset (702).
Creator
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Joseph, Sunny.
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<a href="https://drive.google.com/file/d/1qRwd-88Gj1YNb6llS1a2fOs2mo6XbE20/view" target="_blank" title="Artificial intelligence based system and method for management, recommendation, mapping of skill /" rel="noreferrer noopener">https://drive.google.com/file/d/1qRwd-88Gj1YNb6llS1a2fOs2mo6XbE20/view</a>
Publisher
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Intellectual Property India
Date
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2021-05-06
Language
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English
Type
The nature or genre of the resource
Patent
-
https://archives.christuniversity.in/files/original/ab09643fd3149f8ad854173b42cf6005.jpg
0b6dfeeadbd8dbf97f5aef738454f6b9
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Patents
Subject
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Patent
Description
An account of the resource
Patent
Patent
Patents
Dublin Core
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Title
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Artificial intelligence based system and method for management, recommendation, mapping of skill /
Subject
The topic of the resource
Computer Science
Description
An account of the resource
Patent Number: 202111054501, Applicant: Durgansh Sharma.Artificial intelligence based system (100) and method for management, recommendation, mapping of skill comprising EmpNet (101), recommender system (102), automated machine learning system (103), skillset dataset (104), optimization system (105), industry interface system (106). The method for management, recommendation, mapping of skill comprising the steps of: a) capturing the required skillset personal data by the panchayat system (701); b) verify the skillset (702).
Creator
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Singhal, Tarun Kumar.
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<a href="https://drive.google.com/file/d/1qRwd-88Gj1YNb6llS1a2fOs2mo6XbE20/view" target="_blank" title="Artificial intelligence based system and method for management, recommendation, mapping of skill /" rel="noreferrer noopener">https://drive.google.com/file/d/1qRwd-88Gj1YNb6llS1a2fOs2mo6XbE20/view</a>
Publisher
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Intellectual Property India
Date
A point or period of time associated with an event in the lifecycle of the resource
2021-05-06
Language
A language of the resource
English
Type
The nature or genre of the resource
Patent
-
https://archives.christuniversity.in/files/original/f902e3dbbd3b414e4daefe873635d185.jpg
0b6dfeeadbd8dbf97f5aef738454f6b9
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Patents
Subject
The topic of the resource
Patent
Description
An account of the resource
Patent
Patent
Patents
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
Artificial intelligence based system and method for management, recommendation, mapping of skill /
Subject
The topic of the resource
Computer Science
Description
An account of the resource
Patent Number: 202111054501, Applicant: Durgansh Sharma.
Artificial intelligence based system (100) and method for management, recommendation, mapping of skill comprising EmpNet (101), recommender system (102), automated machine learning system (103), skillset dataset (104), optimization system (105), industry interface system (106). The method for management, recommendation, mapping of skill comprising the steps of: a) capturing the required skillset personal data by the panchayat system (701); b) verify the skillset (702).
Creator
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Sebastian, Shoney.
Source
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<a href="https://drive.google.com/file/d/1qRwd-88Gj1YNb6llS1a2fOs2mo6XbE20/view" target="_blank" title="Artificial intelligence based system and method for management, recommendation, mapping of skill /" rel="noreferrer noopener">https://drive.google.com/file/d/1qRwd-88Gj1YNb6llS1a2fOs2mo6XbE20/view</a>
Publisher
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Intellectual Property India
Date
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2021-05-06
Language
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English
Type
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Patent
-
Dublin Core
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Title
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
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Title
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Artificial Intelligence based System in Protein Folding using Alphafold
Subject
The topic of the resource
3D structure; AlphaFold; Artificial Intelligence; Biomolecules; Molecule; Protein folds
Description
An account of the resource
Artificial Intelligence has a high potential to solve many real-world problems. In the recent years researchers are dealing with one of the biggest complications in biology, which is protein folding. With the assistance of technology, we can foresee how proteins fold from a chain of amino acids into 3D shapes that do life's errands. There are mainly three big problems associatedwith folding of proteins. The first problem is there any particular folding code. The second one there is a folding system. Then the final problem is we able to determine the 3D structure of proteins. Proteins are the microscopic machines and structural building blocks of our cells. They carry out important functions like breaking down foods, storing oxygen and forming scaffolds to help cells keep their shape. Each one is built up of one amino acid chain that folds in on itself into a mostly defined structure. Each part of our body and in any other organism is made either from or by proteins and this is true for every living creature, even for viruses. The structure of very small proteins can be foreseen using the computer method. This article is all about the protein folding problem with more spotlights on the role of AI-based systems in protein structure forecasts. The motivation behind this article is to convey an overall understanding of AI-based answers for protein folding problems. 2022 IEEE
Creator
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Srivastava P.; Suyash S.; Jayapandian N.
Source
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International Conference on Automation, Computing and Renewable Systems, ICACRS 2022 - Proceedings, pp. 750-755.
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Date
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2022-01-01
Identifier
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<a href="https://doi.org/10.1109/ICACRS55517.2022.10029190" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICACRS55517.2022.10029190</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148335158&doi=10.1109%2FICACRS55517.2022.10029190&partnerID=40&md5=d42212fca648e245849dd3f730b498d5" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148335158&doi=10.1109%2fICACRS55517.2022.10029190&partnerID=40&md5=d42212fca648e245849dd3f730b498d5</a>
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Restricted Access
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ISBN: 978-166546084-2
Format
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Online
Language
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English
Type
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Conference paper
Coverage
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Srivastava P., Department of CSE, CHRIST (Deemed to be University), India; Suyash S., Indian Institute of Technology, Indian School of Mines, Jharkhand, India; Jayapandian N., Department of CSE, CHRIST (Deemed to be University), India
-
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Title
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Creator
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Chandar S, Kumar
Title
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Artificial Intelligence Driven Air Quality Prediction for Sustainable Goa
Date
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01-01-2025
Source
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Proceedings of 3rd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2025;pp.1181-1192
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<a href="https://doi.org/10.1109/ICAISS61471.2025.11041723" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICAISS61471.2025.11041723</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010755728?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010755728?origin=resultslist</a>
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Chandar S K., Christ University, School of Business and Management, India
Description
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Clean Air is essential for the health and survival of both humans and wildlife. Air pollution has been linked to various serious diseases, including cancer. Rapid industrial growth and increasing population have contributed to rising pollution from transportation, industries, and agriculture. As a result, air pollution has become a major issue, particularly in developing countries like India. To ensure good air quality, accurate and reliable monitoring and prediction are required. Machine Learning (ML) models have shown promise in predicting Air Quality Index (AQI) over traditional methods. This research aims to propose a AQI prediction model using Attention based Bi-directional Long Short-Term Memory (ABiLSTM) to predict AQI in various cities across Goa, India. Data processing methods are used to manage date before providing it into the ABiLSTM model. Daily AQI series from 2022 to 2024 for six cities in Goa- Panaji, Pond, Assanora, Codli, Tilamol, and Tuem are collected and utilized to verify the proposed model. Two models are tested, including BiLSTM and ABiLSTM. Experimental results showed that the ABiLSTM model outperformed BiLSTM model in all cities, reporting lower error values and higher R2 scores. A comprehensive analysis with a set of evaluation indices confirmed that the proposed ABiLSTM model effectively captures the characteristics of the original AQI series and achieves a higher accuracy in AQI prediction. 2025 IEEE.
Subject
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Air Quality Index Prediction; Attention Based bidirectional Short-Term Memory; Deep Learning; Machine Learning
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833150724-4;
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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Faculty Publications
Article
Faculty Publications -Articles
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Sudha, Y.; Radhika, K.R.; Chethana, C.; Anoop, G.L.; Varma, Gadhiraju Tej; Bisalapur, Sahana
Title
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Artificial Intelligence Driven Drug Delivery Systems: Recent Advances and Emerging Trends
Date
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01-01-2026
Source
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International Journal of Drug Delivery Technology;Volume;16;Issue;9;pp.826-834
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<a href="https://doi.org/10.25258/Ijddt.16.9s.86" target="_blank" rel="noreferrer noopener">https://doi.org/10.25258/Ijddt.16.9s.86</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033122012?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033122012?origin=resultslist</a>
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Sudha Y., Department of Computer Science and Business Systems, Nitte Meenakshi Institute of Technology (NMIT), Nitte(Deemed to be University), Bengaluru, India; Radhika K.R., Department of CSE, BMSIT&M, Karnataka, Bengaluru, India; Chethana C., Department of CSE, BMSIT&M, Karnataka, Bengaluru, India; Anoop G.L., Department of CSE, Christ University, Karnataka, Bengaluru, India; Varma G.T., Department of IT, SRKR Engineering College, Andhra Pradesh, Bhimavaram, India; Bisalapur S., Department of CSE, Department of Computer Science and Engineering, S. G. Balekundri Institute of Technology, Affiliated to VTU, Belagavi, India
Description
An account of the resource
Drug Delivery Systems (DDS) play a critical role in ensuring the therapeutic efficacy and safety of pharmaceutical agents. Conventional drug delivery approaches often suffer from limitations such as poor bioavailability, non-specific targeting, and systemic toxicity. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have revolutionized the design and optimization of drug delivery platforms. AI-driven methods enable predictive modeling, intelligent nanocarrier design, and personalized therapeutic strategies by analyzing large biomedical datasets. These technologies facilitate optimized drug formulation, controlled release mechanisms, and targeted delivery, thereby improving treatment outcomes. AI algorithms such as Support Vector Machines (SVM), random forests, Convolutional Neural Networks (CNN), and reinforcement learning are increasingly applied in nanoparticle design, pharmacokinetic modeling, and clinical decision support systems. Additionally, emerging concepts such as self-driving laboratories, autonomous drug delivery systems, and AI-guided nanomedicine are reshaping pharmaceutical research. This review provides a comprehensive analysis of recent advances in AI-driven drug delivery systems, covering computational techniques, nanocarrier optimization, clinical applications, and emerging research trends. Comparative analysis tables summarize key algorithms, delivery platforms, and research developments reported in the literature. Finally, major challenges including data quality, regulatory issues, and interpretability of AI models are discussed along with future directions for the integration of AI in precision medicine and smart therapeutics. 2026, Dr. Yashwant Research Labs Pvt. Ltd. All rights reserved.
Subject
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Artificial Intelligence; Drug Delivery Systems; Machine Learning; Nanomedicine; Precision Medicine; Smart Therapeutics
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Dr. Yashwant Research Labs Pvt. Ltd.
Relation
A related resource
ISSN: 9754415;
Language
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English
Type
The nature or genre of the resource
Article
Rights
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All Open Access; Bronze Open Access
Format
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online
-
Dublin Core
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Title
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Faculty Publications
Article
Faculty Publications -Articles
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Randhawa, Princy; Adusumilli, Sri Bhargav Krishna; Poddutoori, Jaipal Reddy; Kapila, Dhiraj; Poddutu, Mahipal; Kumar, Satish; Bongale, Arun Kumar; Devesh, Sonal
Title
A name given to the resource
Artificial Intelligence Driven Integrated Hydraulic and Pneumatic Pressure Control Systems for Advanced Regulation of Shut-off Valves
Date
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01-01-2025
Source
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Engineered Science;Volume;35;Issue;;Article No.;1427;
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<a href="https://doi.org/10.30919/es1427" target="_blank" rel="noreferrer noopener">https://doi.org/10.30919/es1427</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010145832?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010145832?origin=resultslist</a>
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Randhawa P., Department of Mechatronics Engineering, Manipal University Jaipur, Rajasthan, Jaipur, 303007, India; Adusumilli S.B.K., Mind Quest Technology Solutions LLC, Phoenix, 85027, AZ, United States; Poddutoori J.R., BBU, 162 Bowery Lane, Downingtown, 19335, PA, United States; Kapila D., Department of Computer Science & Engineering, Lovely Professional University, Punjab, Phagwara, 144411, India; Poddutu M., Penske Corporation, 727 Houston St., Downingtown, 19335, PA, United States; Kumar S., Symbiosis Institute of Technology, Symbiosis International Deemed University, Maharashtra, Pune, 412115, India; Bongale A.K., Symbiosis Institute of Technology, Symbiosis International Deemed University, Maharashtra, Pune, 412115, India; Devesh S., School of Management, Christ University, Karnataka, Bengaluru, 560029, India
Description
An account of the resource
This study presents the design and implementation of an Integrated Hydraulic and Pneumatic Pressure Control System (IHPPCS), emphasizing the development of advanced regulating and shut-off valves (ARASVs) to improve precision, responsiveness, and operational safety across diverse industrial processes. By synergistically combining the strengths of hydraulic and pneumatic technologies, the proposed system addresses critical limitations of conventional fluid control methods, offering enhanced adaptability under dynamic load conditions. The Advanced Regulating and Shut-Off Valve (ARASV) serves as the system's core component, incorporating precision-engineered mechanismssuch as spring-loaded diaphragms and pistonsto regulate fluid pressure with high accuracy. Regulating valves ensure consistent pressure levels, while shutoff valves function as critical safety devices, instantly isolating flow during overpressure events. This dual-function architecture enhances both performance stability and system protection. Technical analysis of the ARASV design reveals significant advantages, including minimal response time lag, reduced hysteresis, and high repeatability under cyclic operation. The integrated approach enables real-time pressure modulation, improving energy efficiency and reducing mechanical wear. Practical evaluations conducted in simulated industrial environments confirm the superior control, fidelity and reliability of the IHPPCS. The findings underscore the transformative potential of hybrid fluid power systems in next-generation industrial automation. By merging the force density of hydraulic systems with the speed and flexibility of pneumatics, the IHPPCS represents a scalable, intelligent solution for complex pressure control requirements. This research contributes to the advancement of sustainable and intelligent control systems, paving the way for more efficient, safe, and adaptive industrial operations. The Author(s) 2025.
Subject
The topic of the resource
Hydraulic systems; Industrial applications; Integrated control; Pneumatic systems; Pressure regulating valves; Shut-off valves
Publisher
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Engineered Science Publisher
Relation
A related resource
ISSN: 2576988X;
Language
A language of the resource
English
Type
The nature or genre of the resource
Article
Rights
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All Open Access; Gold Open Access
Format
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online
-
Dublin Core
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Title
A name given to the resource
Book Chapter
Book Chapter
Faculty Publications- Book Chapter
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Artificial Intelligence Empowered Smart Manufacturing for Modern Society: A Review
Subject
The topic of the resource
Artificial intelligence; Smart era; Smart manufacturing; Society 5.0
Description
An account of the resource
Artificial Intelligence (AI) has emerged as a transformative force in the realm of smart manufacturing, shaping the landscape of modern society. This paper delves into the application of AI in smart manufacturing and its profound impact on various aspects of society, from industrial processes to daily life. We discuss how AI-driven technologies optimize efficiency, sustainability, and quality in manufacturing, enabling Society 5.0s vision of a harmonious convergence between technology and humanity. From intelligent automation to predictive analytics and personalized experiences, we uncover the multifaceted role of AI in shaping the future of smart manufacturing and its broader implications for a modern, interconnected society. 2024 Scrivener Publishing LLC.
Creator
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Tyagi A.K.; Bhatt P.; Chidambaram N.; Kumari S.
Source
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Artificial Intelligence-Enabled Digital Twin for Smart Manufacturing, pp. 55-83.
Publisher
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wiley
Date
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2025-01-01
Identifier
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<a href="https://doi.org/10.1002/9781394303601.ch4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9781394303601.ch4</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206657306&doi=10.1002%2F9781394303601.ch4&partnerID=40&md5=be40ce68eebaadc5178d16b6f4cb35f5" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206657306&doi=10.1002%2f9781394303601.ch4&partnerID=40&md5=be40ce68eebaadc5178d16b6f4cb35f5</a>
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Restricted Access
Relation
A related resource
ISBN: 978-139430360-1; 978-139430357-1
Format
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Online
Language
A language of the resource
English
Type
The nature or genre of the resource
Book chapter
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Tyagi A.K., Department of Fashion Technology, National Institute of Fashion Technology, New Delhi, India; Bhatt P., CSE Department, PIET, Parul University, Gujarat, India; Chidambaram N., School of Business and Management, CHRIST (Deemed to be University), Maharashtra, Pune, India; Kumari S., Department of Computer Science, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, Chennai, India
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Book Chapter
Book Chapter
Faculty Publications- Book Chapter
Dublin Core
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Title
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Artificial Intelligence for Bio-Inspired Security
Description
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[No abstract available]
Creator
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Nanjundan P.; Karpagam C.
Source
A related resource from which the described resource is derived
Bio-Inspired Computational Paradigms: Security and Privacy in Dynamic Smart Networks, pp. 121-137.
Publisher
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CRC Press
Date
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2024-01-01
Identifier
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<a href="https://doi.org/10.1201/9781003248545-8" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003248545-8</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198298118&doi=10.1201%2F9781003248545-8&partnerID=40&md5=d61fcb2782e683eb9182d781ccc5bb9e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198298118&doi=10.1201%2f9781003248545-8&partnerID=40&md5=d61fcb2782e683eb9182d781ccc5bb9e</a>
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Restricted Access
Relation
A related resource
ISBN: 978-104004450-6; 978-103216429-8
Format
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Online
Language
A language of the resource
English
Type
The nature or genre of the resource
Book chapter
Coverage
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Nanjundan P., Christ University, Lavasa, India; Karpagam C., Dr. N.G.P. Arts and Science College, Coimbatore, India
-
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Title
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Book
Book
Faculty Publications- Book
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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Artificial intelligence for blockchain and cybersecurity powered IoT applications
Description
An account of the resource
The objective of this book is to showcase recent solutions and discuss the opportunities that AI, blockchain, and even their combinations can present to solve the issue of Internet of Things (IoT) security. It delves into cuttingedge technologies and methodologies, illustrating how these innovations can fortify IoT ecosystems against security threats. The discussion includes a comprehensive analysis of AI techniques such as machine learning and deep learning, which can detect and respond to security breaches in real time. The role of blockchain in ensuring data integrity, transparency, and tamper-proof transactions is also thoroughly examined. Furthermore, this book will present solutions that will help analyze complex patterns in user data and ultimately improve productivity. 2025, Mariya Ouaissa, Mariyam Ouaissa, Zakaria Boulouard, Abhishek Kumar, Vandana Sharma and Keshav Kaushik. All rights reserved.
Creator
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Ouaissa M.; Ouaissa M.; Boulouard Z.; Kumar A.; Sharma V.; Kaushik K.
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Artificial Intelligence for Blockchain and Cybersecurity Powered IoT Applications, pp. 1-269.
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CRC Press
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2025-01-01
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<a href="https://doi.org/10.1201/9781003497585" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003497585</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208867477&doi=10.1201%2F9781003497585&partnerID=40&md5=d512f37744ae647ce920226d4d4d7028" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208867477&doi=10.1201%2f9781003497585&partnerID=40&md5=d512f37744ae647ce920226d4d4d7028</a>
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ISBN: 978-104026514-7; 978-103280238-1
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Online
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English
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Book
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Ouaissa M., Cadi Ayyad University, Marrakech, Morocco; Ouaissa M., Chouaib Doukkali University, El Jadida, Morocco; Boulouard Z., Department of Computer Sciences, Faculty of Sciences and Techniques Mohammedia, Hassan II University, Casablanca, Morocco; Kumar A., Computer Science and Engineering Department, Chandigarh University, Punjab, India; Sharma V., CHRIST (Deemed to be University), Delhi, India; Kaushik K., Amity School of Engineering and Technology, Amity University Punjab, India
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Faculty Publications
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Ouaissa, Mariya; Ouaissa, Mariyam; Boulouard, Zakaria; Kumar, Abhishek; Sharma, Vandana; Kaushik, Keshav
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Artificial intelligence for blockchain and cybersecurity powered IoT applications
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01-01-2025
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Artificial Intelligence for Blockchain and Cybersecurity Powered IoT Applications;pp.1-269
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<a href="https://doi.org/10.1201/9781003497585" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003497585</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85208867477?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85208867477?origin=resultslist</a>
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Ouaissa M., Cadi Ayyad University, Marrakech, Morocco; Ouaissa M., Chouaib Doukkali University, El Jadida, Morocco; Boulouard Z., Department of Computer Sciences, Faculty of Sciences and Techniques Mohammedia, Hassan II University, Casablanca, Morocco; Kumar A., Computer Science and Engineering Department, Chandigarh University, Punjab, India; Sharma V., CHRIST (Deemed to be University), Delhi, India; Kaushik K., Amity School of Engineering and Technology, Amity University Punjab, India
Description
An account of the resource
The objective of this book is to showcase recent solutions and discuss the opportunities that AI, blockchain, and even their combinations can present to solve the issue of Internet of Things (IoT) security. It delves into cuttingedge technologies and methodologies, illustrating how these innovations can fortify IoT ecosystems against security threats. The discussion includes a comprehensive analysis of AI techniques such as machine learning and deep learning, which can detect and respond to security breaches in real time. The role of blockchain in ensuring data integrity, transparency, and tamper-proof transactions is also thoroughly examined. Furthermore, this book will present solutions that will help analyze complex patterns in user data and ultimately improve productivity. 2025, Mariya Ouaissa, Mariyam Ouaissa, Zakaria Boulouard, Abhishek Kumar, Vandana Sharma and Keshav Kaushik. All rights reserved.
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CRC Press
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ISBN: 978-104026514-7; 978-103280238-1;
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English
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Book
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Restricted Access; Hardcopy may be available in the library
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Title
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Book
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Artificial Intelligence for Cyber Defense and Smart Policing
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The future policing ought to cover identification of new assaults, disclosure of new ill-disposed patterns, and forecast of any future vindictive patterns from accessible authentic information. Such keen information will bring about building clever advanced proof handling frameworks that will help cops investigate violations. Artificial Intelligence for Cyber Defense and Smart Policing will describe the best way of practicing artificial intelligence for cyber defense and smart policing. Salient Features: Combines AI for both cyber defense and smart policing in one place Covers novel strategies in future to help cybercrime examinations and police Discusses different AI models to fabricate more exact techniques Elaborates on problematization and international issues Includes case studies and real-life examples This book is primarily aimed at graduates, researchers, and IT professionals. Business executives will also find this book helpful. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors.
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Vijayalakshmi S.; Durgadevi P.; Jacob L.; Balusamy B.; Nand P.
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Artificial Intelligence for Cyber Defense and Smart Policing, pp. 1-178.
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CRC Press
Date
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2024-01-01
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<a href="https://doi.org/10.1201/9781003251781" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003251781</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190201292&doi=10.1201%2F9781003251781&partnerID=40&md5=7733e16336fb9da0793a6aa065ecfc0e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190201292&doi=10.1201%2f9781003251781&partnerID=40&md5=7733e16336fb9da0793a6aa065ecfc0e</a>
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ISBN: 978-100381553-2; 978-103217093-0
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Online
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English
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Book
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Vijayalakshmi S., Data Science Department in Christ (Deemed to be University), Lavasa Campus, Pune, India; Durgadevi P., Department of Computer Science and Engineering, GITAM University (Deemed to be University), Bengaluru, India; Jacob L., Department of Data Science, Christ (Deemed to be University), Lavasa, Pune, India; Balusamy B., School of Computing Sciences and Engineering, Galgotias University, Greater Noida, India; Nand P.
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Reviews
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Title
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Artificial intelligence for diabetic retinopathy detection: A systematic review
Subject
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Artificial intelligence; Deep learning; Diabetic retinopathy; Machine learning; Ophthalmologist; Proliferative diabetic retinopathy vision loss
Description
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The incidence of diabetic retinopathy (DR) has increased at a rapid pace in recent years all over the world. Diabetic eye illness is identified as one of the most common reasons for vision loss among people. To properly manage DR, there has been immense research and exploration of state-of-the-art methods using artificial intelligence (AI) enabled models. Specifically, AI-empowered models combine multiple machine learning (ML) and deep learning (DL) based algorithms to improve the performance of the developed system architectures that are commercially utilized for the detection of DR disease. However, these models still exhibit several limitations, such as computational complexity, low accuracy in DR stage detection due to class imbalance, more time consumption, and high maintenance cost. To overcome these limits, a more advanced model is required to accurately predict the DR stage in the initial stages. For example, the identification of DR disease in the initial stage helps the ophthalmologist to make an accurate and safe diagnosis, and thereby, eyesight-related issues may be treated more effectively. This study conducted a systematic literature review (SLR) to provide a detailed discussion of the background of diabetic retinopathy, its major causes, challenges faced by ophthalmologists in DR detection, and possible solutions for identifying DR in the initial stage. Also, the SLR provides an in-depth analysis of the existing state-of-the-art techniques and system models used in DR diagnosis based on AI, ML, and recently developed DL-based approaches. Furthermore, this present survey would be helpful for the research community to receive information on the recent approaches used for DR identification along with their significant challenges and limitations. 2024 The Authors
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Senapati A.; Tripathy H.K.; Sharma V.; Gandomi A.H.
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Informatics in Medicine Unlocked, Vol-45
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Elsevier Ltd
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2024-01-01
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<a href="https://doi.org/10.1016/j.imu.2024.101445" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.imu.2024.101445</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184074368&doi=10.1016%2Fj.imu.2024.101445&partnerID=40&md5=3a2ba61b0340ffd8d7ce2bfb3d336c77" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184074368&doi=10.1016%2fj.imu.2024.101445&partnerID=40&md5=3a2ba61b0340ffd8d7ce2bfb3d336c77</a>
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All Open Access; Gold Open Access
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ISSN: 23529148
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Online
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English
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Review
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Senapati A., School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, 751024, India; Tripathy H.K., School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, 751024, India; Sharma V., Department of Computational Sciences, CHRIST(Deemed to be University), Delhi-NCR Campus, India; Gandomi A.H., Faculty of Engineering and IT, University of Technology Sydney, Ultimo, 2007, NSW, Australia, University Research and Innovation Center (EKIK), uda University, Budapest, 1034, Hungary
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Faculty Publications
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Velchamy, Ilango; Fukey, Leena N.; Uma, V.R.; Srinivasan, Kaliyaperumal
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ARTIFICIAL INTELLIGENCE FOR DIGITAL TALENT ACQUISITION AND MANAGEMENT :Analytical Approaches, Practices, Models for Digitalization
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01-01-2026
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Artificial Intelligence for Digital Talent Acquisition and Management: Analytical Approaches, Practices, Models for Digitalization;pp.1-112
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<a href="https://www.scopus.com/pages/publications/105034468358?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034468358?origin=resultslist</a>
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Velchamy I., CMR Institute of Technology, India; Fukey L.N., Christ University, India; Uma V.R., Christ University, India; Srinivasan K., University of Technology and Applied Science, Oman
Description
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In an era of rapid technological innovation and digital transformation, artificial intelligence (AI) is reshaping the way organizsations approach talent acquisition and management. Artificial Intelligence for Digital Talent Acquisition and Management: Analytical Approaches, Practices, Models for Digitalization analyses the intersection of AI and workforce strategies, offering readers a thorough exploration of analytical approaches, cutting-edge practices, and innovative models. Bridging the gap between AI theory and practical application, the authors provide actionable frameworks and strategies for leveraging AI in workforce management. From streamlining talent acquisition to enhancing management practices, the authors present a comprehensive roadmap for navigating the transformative impact of AI on digital talent strategies. Whether you're a business leader, HR professional, or academic, this book equips you with the tools and knowledge to thrive in the ever-evolving landscape of digitalization. Editorial matter and selection 2026 Ilango Velchamy, Leena N. Fukey, V. R. Uma, and Kaliyaperumal Srinivasan.
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Emerald Group Publishing Ltd.
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ISBN: 978-183708884-3; 978-183708886-7; 978-183708885-0;
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English
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Book
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Conference Papers
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Artificial Intelligence for Enhanced Anti-Money Laundering and Asset Recovery: A New Frontier in Financial Crime Prevention
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Anti-Money Laundering (AML); Artificial Intelligence (AI); Machine Learning Algorithms; Suspicious Activity Reports (SARs); Transaction Monitoring
Description
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The incorporation of artificial intelligence (AI) into asset recovery and anti-money laundering (AML) procedures signifies a revolutionary change in the handling of financial crime. This article investigates the use of AI techniques to improve AML compliance, detect suspicious activity, and improve transaction monitoring. Financial institutions can now analyze massive volumes of transaction data in real-time, find anomalies, and lower false positives thanks to AI-based solutions, which include machine learning algorithms and predictive modeling. The research also outlines the difficulties and advantages of implementing AI, such as enhancing the effectiveness and caliber of suspicious activity reports (SARs) while resolving security and privacy issues with data. The study makes the case that AI's capacity to offer collaborative analytics and dynamic risk assessments is essential for the development of AML frameworks and the overall effectiveness of financial crime prevention. 2024 IEEE.
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Balaji K.
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2nd International Conference on Intelligent Cyber Physical Systems and Internet of Things, ICoICI 2024 - Proceedings, pp. 1010-1016.
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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/ICoICI62503.2024.10696100" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICoICI62503.2024.10696100</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207230856&doi=10.1109%2FICoICI62503.2024.10696100&partnerID=40&md5=c64ed3d6e020203c16abf10d7c791e64" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207230856&doi=10.1109%2fICoICI62503.2024.10696100&partnerID=40&md5=c64ed3d6e020203c16abf10d7c791e64</a>
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ISBN: 979-833154066-1
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Online
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English
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Conference paper
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Balaji K., Christ University, Business and Management, Bengaluru, India
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Faculty Publications
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Syed, Waheeduddin Khadri; Mohammed, Abubakar; Reddy, Janamolla Kavitha; Gupta, Ketan; Logeshwaran, J.
Title
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Artificial Intelligence in Banking Security-Technical Innovations and Challenges
Date
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01-01-2025
Source
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Proceedings of 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2025;pp.170-176
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<a href="https://doi.org/10.1109/ICICV64824.2025.11085795" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICICV64824.2025.11085795</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105013074043?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105013074043?origin=resultslist</a>
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Syed W.K., University of the Cumberlands, Dept. of Information Technology, Somerville, NJ, United States; Mohammed A., University of the Cumberlands, Dept. of Information Technology, Houston, TX, United States; Reddy J.K., University of the Cumberlands - IT, Hillsborough, NJ, United States; Gupta K., University of the Cumberlands, Dept. of Information Technology, Williamsburg, KY, United States; Logeshwaran J., Christ University, Department of Computer Science, Karnataka, Bangluru, 560029, India
Description
An account of the resource
The accelerating adoption of artificial intelligence (AI) technologies in the banking sector has introduced transformative possibilities for enhancing security frameworks against increasingly sophisticated cyber threats. This research investigates the technical innovations driven by AI, such as machine learning algorithms, biometric authentication systems, and natural language processing, and their impact on improving fraud detection, cybersecurity monitoring, and compliance automation. The paper identifies how AI systems, through real-time analysis of large-scale transaction data, can locate abnormal behavioral patterns and respond proactively to potential threats, significantly reducing human error and response time. A detailed analysis of the current literature reveals a significant research gap in integrating explainable AI, secure data governance frameworks, and scalable models suited for diverse banking environments. The outcome of this research highlights the need for a balanced approach that fosters technological innovation while addressing regulatory compliance, ethical concerns, and operational constraints, paving the way for a secure and intelligent banking infrastructure. 2025 IEEE.
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Artificial Intelligence; Banking Security; Cybersecurity; Machine Learning; Natural Language Processing
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833151175-3;
Language
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English
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Conference paper
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Restricted Access; Hardcopy may be available in the library
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Conference Papers
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Dublin Core
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Artificial Intelligence in Data-Driven Analytics for the Personalized Healthcare
Subject
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Big Data Analytics; Data-driven analytics; Data-driven medicine; Digital health; Expert systems; Health intelligence; smart healthcare
Description
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Among the various developments in progress over the last decade, we have seen the generous growth of information investigation to take care of, plan, and use a lot of information beneficially. Be that as it may, because the analysis of evidence will only operate for authentic information and have findings as predefined by individuals, explicit principle-based calculations have been developed to broaden the investigation of information, 'Which is usually referred to as 'AI'. AI didn't expect PCs to be personalized unambiguously, which is a definite bit of leeway. In order to break down information and construct complicated equations to foresee models, which was called prescient analysis, AI was then joined with information inquiry. A set of laws characterized by persons, known as prescient equations, drive the prescient inquiry, and are used to break down genuine knowledge in order to predict potential outcomes. 2021 IEEE.
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Sivabalaselvamani D.; Selvakarthi D.; Yogapriya J.; Thiruvenkatasuresh M.P.; Maruthappa M.; Chandra A.S.
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2021 International Conference on Computer Communication and Informatics, ICCCI 2021
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Institute of Electrical and Electronics Engineers Inc.
Date
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2021-01-01
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<a href="https://doi.org/10.1109/ICCCI50826.2021.9402703" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCCI50826.2021.9402703</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104943960&doi=10.1109%2FICCCI50826.2021.9402703&partnerID=40&md5=5d81a375916441f11b764c3ecf9c7034" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104943960&doi=10.1109%2fICCCI50826.2021.9402703&partnerID=40&md5=5d81a375916441f11b764c3ecf9c7034</a>
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Restricted Access
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ISBN: 978-172815875-4
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Online
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English
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Conference paper
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Sivabalaselvamani D., Kongu Engineering College Perundurai, Department of Computer Applications, Erode, Tamilnadu, 638060, India; Selvakarthi D., Kongu Engineering College Perundurai, Department of Electronics and Instrumentation Engineering, Erode, Tamilnadu, 638060, India; Yogapriya J., Kongunadu College of Engineering and Technology, Department of Computer Science and Engineering, Trichy, Tamilnadu, India; Thiruvenkatasuresh M.P., Erode Sengunthar Engineering College, Department of Computer Science and Engineering, Erode, Tamilnadu, India; Maruthappa M., School of Engineering and Technology, Sunway University, Malaysia; Chandra A.S., Institute of Management, Christ University (Deemed to Be University), Bengaluru, Karnataka, India
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Ali, Khan Afrin Riyasat; Somu, C.S.
Title
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Artificial Intelligence in Detecting and Mitigating Online Child Sexual Abuse: Approaches and Solutions
Date
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01-01-2025
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Proceedings - 2025 2nd International Conference on Intelligent Computing and Robotics, ICICR 2025;pp.464-471
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<a href="https://doi.org/10.1109/ICICR65456.2025.00087" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICICR65456.2025.00087</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018744795?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018744795?origin=resultslist</a>
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Ali K.A.R., Christ University, Bengaluru, India; Somu C.S., Christ University, Bengaluru, India
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Previous research papers have discussed whether Artificial Intelligence (AI) -based tools like Chat Bots, Law-U Model, and Sweetie. 20 have the potential to mitigate online child sexual abuse. The literature review indicates that AI tools promise good intervention and prevention strategies for several tech-based companies like Google and Microsoft. However, there is a lack of systematic study on AI tools' potential uses, limitations, and legal risks. This paper conducts a systematic literature review to explore the uses and limitations of AI-based interventions in combating online child sexual abuse. It explores the legal and ethical risks of deploying such technological innovations from the viewpoint of data protection, privacy, and security. The authors use the PRISMA technique and thematically answer the research questions. Data are collected from reliable sources such as Statista and the World Health Organisation. The findings of this paper highlight the potential uses of AI for law agencies, forensic experts, victims, and technology companies. The research reports the absence of a sufficient legal framework for the governance and accountability of AI tools. The findings further indicate the need for clarification in the law regarding the legal status of AI tools like Sweetie 2.0. Lastly, this paper offers a framework for harmonizing AI usage with human rights standards. 2025 IEEE.
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Artificial Intelligence; Data Privacy; Online Child Sexual Abuse; Security and PRISMA
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833154429-4;
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English
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Conference paper
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Restricted Access; Hardcopy may be available in the library
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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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Artificial intelligence in developing countries: The impact of generative artificial intelligence (AI) technologies for development
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artificial intelligence; developing countries; fourth industrial revolution; generative AI; technological change
Description
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This paper explores the potential impact of Generative Artificial Intelligence (Generative AI) on developing countries, considering both positive and negative effects across various domains of information, culture, and industry. Generative Artificial Intelligence refers to artificial intelligence (AI) systems that generate content, such as text, audio, or video, aiming to produce novel and creative outputs based on training data. Compared to conversational artificial intelligence, generative artificial intelligence systems have the unique capability of not only providing replies but also generating the content of those responses. Recent advancements in Artificial Intelligence during the Fourth Industrial Revolution, exemplified by tools like ChatGPT, have gained popularity and reshaped content production and creation. However, the benefits of generative artificial intelligence are not equally accessible to all, especially in developing countries, where limited access to cutting-edge technologies and inadequate infrastructure pose challenges. This paper seeks to understand the potential impact of generative AI technologies on developing countries, considering economic growth, access to technology, and the potential paradigm shift in education, healthcare, and the environment. The findings emphasize the importance of providing the necessary support and infrastructure to ensure that generative AI contributes to inclusive development rather than deepening existing inequalities. The study highlights the significance of integrating Generative AI into the context of the Fourth Industrial Revolution in developing countries, where technological change is a crucial determinant of progress and equitable growth. The Author(s) 2023.
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Mannuru N.R.; Shahriar S.; Teel Z.A.; Wang T.; Lund B.D.; Tijani S.; Pohboon C.O.; Agbaji D.; Alhassan J.; Galley J.; Kousari R.; Ogbadu-Oladapo L.; Saurav S.K.; Srivastava A.; Tummuru S.P.; Uppala S.; Vaidya P.
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Information Development
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SAGE Publications Ltd
Date
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2023-01-01
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<a href="https://doi.org/10.1177/02666669231200628" target="_blank" rel="noreferrer noopener">https://doi.org/10.1177/02666669231200628</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171461674&doi=10.1177%2F02666669231200628&partnerID=40&md5=923603e6a41e13d5a2b196c25911bc20" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171461674&doi=10.1177%2f02666669231200628&partnerID=40&md5=923603e6a41e13d5a2b196c25911bc20</a>
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Restricted Access
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ISSN: 2666669
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Online
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English
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Article
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Mannuru N.R., University of North Texas, Denton, TX, United States; Shahriar S., University of Guelph, Guelph, ON, Canada; Teel Z.A., University of North Texas, Denton, TX, United States; Wang T., Emporia State University, Emporia, KS, United States; Lund B.D., University of North Texas, Denton, TX, United States; Tijani S., Nigerian Institute of Social and Economic Research, Ibadan, Nigeria; Pohboon C.O., University of North Texas, Denton, TX, United States; Agbaji D., University of North Texas, Denton, TX, United States; Alhassan J., University of North Texas, Denton, TX, United States; Galley J., Emporia State University, Emporia, KS, United States; Kousari R., Department of Medical Library and Information Science, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran; Ogbadu-Oladapo L., University of North Texas, Denton, TX, United States; Saurav S.K., Documentation Research and Training Centre, Banglore, India; Srivastava A., Seth M.R. Jaipuria School, Rajajipuram Campus, Lucknow, India; Tummuru S.P., University of North Texas, Denton, TX, United States; Uppala S., University of North Texas, Denton, TX, United States; Vaidya P., CHRIST University Pune Lavasa India, Maharashtra, 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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Faculty Publications -Articles
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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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Mannuru, Nishith Reddy; Shahriar, Sakib; Teel, ZoA; Wang, Ting; Lund, Brady D; Tijani, Solomon; Pohboon, Chalermchai Oak; Agbaji, Daniel; Alhassan, Joy; Galley, JaKLyn; Kousari, Raana; Ogbadu-Oladapo, Lydia; Saurav, Shubham Kumar; Srivastava, Aishwarya; Tummuru, Sai Priya; Uppala, Sravya; Vaidya, Praveenkumar
Title
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Artificial intelligence in developing countries: The impact of generative artificial intelligence (AI) technologies for development
Date
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01-01-2025
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Information Development;Volume;41;Issue;3 Special Issue: Artificial Intelligence initiatives;pp.1036-1054
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<a href="https://doi.org/10.1177/02666669231200628" target="_blank" rel="noreferrer noopener">https://doi.org/10.1177/02666669231200628</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85171461674?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85171461674?origin=resultslist</a>
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Mannuru N.R., University of North Texas, Denton, TX, United States; Shahriar S., University of Guelph, Guelph, ON, Canada; Teel Z.A., University of North Texas, Denton, TX, United States; Wang T., Emporia State University, Emporia, KS, United States; Lund B.D., University of North Texas, Denton, TX, United States; Tijani S., Nigerian Institute of Social and Economic Research, Ibadan, Nigeria; Pohboon C.O., University of North Texas, Denton, TX, United States; Agbaji D., University of North Texas, Denton, TX, United States; Alhassan J., University of North Texas, Denton, TX, United States; Galley J., Emporia State University, Emporia, KS, United States; Kousari R., Department of Medical Library and Information Science, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran; Ogbadu-Oladapo L., University of North Texas, Denton, TX, United States; Saurav S.K., Documentation Research and Training Centre, Banglore, India; Srivastava A., Seth M.R. Jaipuria School, Rajajipuram Campus, Lucknow, India; Tummuru S.P., University of North Texas, Denton, TX, United States; Uppala S., University of North Texas, Denton, TX, United States; Vaidya P., CHRIST University Pune Lavasa India, Maharashtra, India
Description
An account of the resource
This paper explores the potential impact of Generative Artificial Intelligence (Generative AI) on developing countries, considering both positive and negative effects across various domains of information, culture, and industry. Generative Artificial Intelligence refers to artificial intelligence (AI) systems that generate content, such as text, audio, or video, aiming to produce novel and creative outputs based on training data. Compared to conversational artificial intelligence, generative artificial intelligence systems have the unique capability of not only providing replies but also generating the content of those responses. Recent advancements in Artificial Intelligence during the Fourth Industrial Revolution, exemplified by tools like ChatGPT, have gained popularity and reshaped content production and creation. However, the benefits of generative artificial intelligence are not equally accessible to all, especially in developing countries, where limited access to cutting-edge technologies and inadequate infrastructure pose challenges. This paper seeks to understand the potential impact of generative AI technologies on developing countries, considering economic growth, access to technology, and the potential paradigm shift in education, healthcare, and the environment. The findings emphasize the importance of providing the necessary support and infrastructure to ensure that generative AI contributes to inclusive development rather than deepening existing inequalities. The study highlights the significance of integrating Generative AI into the context of the Fourth Industrial Revolution in developing countries, where technological change is a crucial determinant of progress and equitable growth. The Author(s) 2023
Subject
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artificial intelligence; developing countries; fourth industrial revolution; generative AI; technological change
Publisher
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SAGE Publications Ltd
Relation
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ISSN: 2666669;
Language
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English
Type
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Article
Rights
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Restricted Access; Hardcopy may be available in the library
Format
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online