103
20
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https://archives.christuniversity.in/files/original/9d8901a678bb4bc5f1ba50b61c199298.pdf
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Artificial intelligence based computational framework for identification and classification of interstitial lung diseases using HRCT images /
Subject
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Computer Science
Creator
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Nidhin Raju - 1981307
Publisher
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CHRIST (Deemed to be University)
Date
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2024-07-19
Language
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English
Type
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PhD
-
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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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Artificial Intelligence Based Enhanced Virtual Mouse Hand Gesture Tracking Using Yolo Algorithm
Subject
The topic of the resource
Artificial Intelligence; Computer Vision; Hand Gestures; Machine Learning; Virtual Mouse; Virtual Reality; YOLO Algorithm
Description
An account of the resource
Virtual mouse technology has revolutionized human computer interaction, allowing users to interact with digital environments without physical peripherals. The concept traces back to the late 1970s, and over the years, it has evolved with significant advancements in computer vision, motion tracking, and gesture recognition technologies. In recent times, machine learning techniques, particularly YOLOv8, have been integrated into virtual mouse technology, enabling accurate and swift detection of virtual objects and surfaces. This advancement enhances seamless interaction, intuitive hand gestures, and personalized virtual reality experiences tailored to individual user preferences. The proposed model, EHT (Enhanced Hand Tracking), leverages the power of YOLOv8 to address the limitations of existing models, such as Mediapipe. EHT achieves higher accuracy in hand tracking, real-Time hand gesture recognition, and improved multi-user interactions. It adapts to users' unique gestures over time, delivering a more natural and immersive computing experience with accuracy rates exceeding those of Mediapipe. For instance, across multiple sample datasets, EHT consistently outperformed Mediapipe in hand tracking accuracy. In Sample Dataset 1, EHT demonstrated an accuracy of 98.3% compared to Mediapipe's 95.65%. Similarly, in Sample Dataset 2, EHT achieved an accuracy of 99.35%, surpassing Mediapipe's 94.63%. Even in Sample Dataset 3, EHT maintained its superiority with an accuracy of 98.54 %, whereas Mediapipe achieved 98.26%. The successful implementation of EHT requires a custom dataset and optimization techniques to ensure efficiency on virtual reality hardware. EHT model is anticipated redefining how users interact with digital environments, unlocking new possibilities for intuitive and immersive computing experiences. 2023 IEEE.
Creator
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Karthick S.; Dinesh M.; Raj C J.D.; Jayapandian N.
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2023 IEEE 2nd International Conference on Data, Decision and Systems, ICDDS 2023
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Institute of Electrical and Electronics Engineers Inc.
Date
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2023-01-01
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<a href="https://doi.org/10.1109/ICDDS59137.2023.10434330" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDDS59137.2023.10434330</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186659487&doi=10.1109%2FICDDS59137.2023.10434330&partnerID=40&md5=cb9a1b136ec3213ba1e17a02f8a28c85" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186659487&doi=10.1109%2fICDDS59137.2023.10434330&partnerID=40&md5=cb9a1b136ec3213ba1e17a02f8a28c85</a>
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ISBN: 979-835031416-8
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Online
Language
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English
Type
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Conference paper
Coverage
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Karthick S., Christ University, Department of CSE, Bangalore, India; Dinesh M., Christ University, Department of CSE, Bangalore, India; Raj C J.D., Christ University, Department of CSE, Bangalore, India; Jayapandian N., Department of CSE Christ University, India
-
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Conference Papers
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Faculty Publications- Conference Papers
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Artificial Intelligence based Semantic Text Similarity for RAP Lyrics
Subject
The topic of the resource
bag-of-words; Continuous Bag of Words; natural language processing; Semantic Textual Similarity (STS); Word2Vec
Description
An account of the resource
Data mining is the primary method of gathering large volumes of knowledge. To make use of such data to implementation requires the use of effective machine learning strategies. Semantic Textual Similarity is one of the primary machine learning strategies. At its core, semantic textual similarity is the identification of words with similar context. Initial work in STS involved text reuse, word search among others. The proposed research work uses a specific method of determining textual similarity using Google's Word2Vec framework and the Continuous-bag-of-words algorithm for identifying word similarity in rap records. A large data set consisting of over 50,000 rap records is used. The key aspect of proposed methodology is to determine the words with similar context and cluster them into different word clusters also called bags. To achieve the desired result, the dataset is first processed to obtain the features. Once the features are selected, a model is generated by passing the data onto the Word2Vec framework. The research work on semantic textual similarity was repeated across three different runs, with the data set size changing in every run. At the end of each the accuracy of similarity obtained by the model was determined. The current research work has achieved average accuracy as 85%. 2020 IEEE.
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Chandra J.; Santhanam A.; Joseph A.
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International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2020
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Date
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2020-01-01
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<a href="https://doi.org/10.1109/ic-ETITE47903.2020.211" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ic-ETITE47903.2020.211</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085197570&doi=10.1109%2Fic-ETITE47903.2020.211&partnerID=40&md5=fc97b9e98d458d6da14c3c764b712ced" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085197570&doi=10.1109%2fic-ETITE47903.2020.211&partnerID=40&md5=fc97b9e98d458d6da14c3c764b712ced</a>
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ISBN: 978-172814142-8
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Online
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English
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Conference paper
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Chandra J., CHRIST Deemed to Be University, Department of Computer Science, Banglore, India; Santhanam A., CHRIST Deemed to Be University, Department of Computer Science, Banglore, India; Joseph A., CHRIST Deemed to Be University, Department of Computer Science, Banglore, India
-
https://archives.christuniversity.in/files/original/e9df10b3fde2fc77e7fb227608beacb5.pdf
d6bb3fadd0549f58bfe6ed52613578fd
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Artificial intelligence based steganography model for social media data set /
Subject
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Computer Science
Creator
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Gurunath R - 1942062
Publisher
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CHRIST (Deemed to be University)
Date
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2023-01-03
Language
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English
Type
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PhD
-
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0b6dfeeadbd8dbf97f5aef738454f6b9
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Title
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Patents
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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).
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Tiwari, Akhilesh.
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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
-
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Title
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Patents
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Patent
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Patent
Patent
Patents
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Title
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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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Sharma, Durgansh.
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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
A language of the resource
English
Type
The nature or genre of the resource
Patent
-
https://archives.christuniversity.in/files/original/de61ed3b25091e98b2c02c6b4ad5678c.jpg
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Patents
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Patent
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Patent
Patent
Patents
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Title
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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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Devassy, Viju P.
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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/b9fc3f6d4dc13afbc3d04e29ba7fcda4.jpg
0b6dfeeadbd8dbf97f5aef738454f6b9
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Title
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Patents
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Patent
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Patent
Patent
Patents
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Title
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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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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
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Patents
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Patent
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Patent
Patent
Patents
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Title
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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
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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
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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
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
A related resource from which the described resource is derived
<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
-
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
A related resource from which the described resource is derived
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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Book Chapter
Book Chapter
Faculty Publications- Book Chapter
Dublin Core
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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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ISBN: 978-139430360-1; 978-139430357-1
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English
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Book chapter
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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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Artificial Intelligence for Bio-Inspired Security
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[No abstract available]
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Nanjundan P.; Karpagam C.
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Bio-Inspired Computational Paradigms: Security and Privacy in Dynamic Smart Networks, pp. 121-137.
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CRC Press
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2024-01-01
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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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ISBN: 978-104004450-6; 978-103216429-8
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English
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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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Artificial intelligence for blockchain and cybersecurity powered IoT applications
Description
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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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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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Title
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Book
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Artificial Intelligence for Cyber Defense and Smart Policing
Description
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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
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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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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
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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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Conference Papers
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Artificial Intelligence for Enhanced Anti-Money Laundering and Asset Recovery: A New Frontier in Financial Crime Prevention
Subject
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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.
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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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Conference Papers
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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.
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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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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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Artificial intelligence in developing countries: The impact of generative artificial intelligence (AI) technologies for development
Subject
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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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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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Conference Papers
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Faculty Publications- Conference Papers
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Artificial Intelligence in Disaster Management: A Survey
Subject
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Disaster management; Disaster mitigation; Disaster preparedness; Disaster recovery; Disaster response; Machine learning
Description
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This paper provides a literature review of cutting-edge artificial intelligence-based methods for disaster management. Most governments are worried about disasters, which, in general, are unbelievable events. Researchers tried to deploy numerous artificial intelligence (AI)-based approaches to eliminate disaster management at different stages. Machine learning (ML) and deep learning (DL) algorithms can manage large and complex datasets emerging intrinsically in disaster management circumstances and are incredibly well suited for crucial tasks such as identifying essential features and classification. The study of existing literature in this paper is related to disaster management, and further, it collects recent development in nature-inspired algorithms (NIA) and their applications in disaster management. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Arora S.; Kumar S.; Kumar S.
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Lecture Notes in Networks and Systems, Vol-552, pp. 793-805.
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Springer Science and Business Media Deutschland GmbH
Date
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2023-01-01
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<a href="https://doi.org/10.1007/978-981-19-6634-7_56" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-19-6634-7_56</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149974082&doi=10.1007%2F978-981-19-6634-7_56&partnerID=40&md5=586c99be7c9aa9c5349f59fb3e46bbbf" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149974082&doi=10.1007%2f978-981-19-6634-7_56&partnerID=40&md5=586c99be7c9aa9c5349f59fb3e46bbbf</a>
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Restricted Access
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ISSN: 23673370; ISBN: 978-981196633-0
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Online
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English
Type
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Conference paper
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Arora S., Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Rajasthan, Rajasthan, Jaipur, 303002, India; Kumar S., Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Rajasthan, Rajasthan, Jaipur, 303002, India; Kumar S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India