AI-Driven Advancements in Scientific Knowledge Dissemination


Authors : Megha Potdar

Volume/Issue : Volume 10 - 2025, Issue 6 - June


Google Scholar : https://tinyurl.com/3sc87t8p

DOI : https://doi.org/10.38124/ijisrt/25jun419

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : The integration of Artificial Intelligence (AI) has ushered in a new era in scientific knowledge dissemination. This abstract delineates the pivotal role played by AI technologies in revolutionizing the process of sharing and accessing scholarly information. By harnessing machine learning, natural language processing, and data analytics, AI facilitates automated literature reviews, intelligent summarization, and personalized content recommendations. Furthermore, it ensures greater accessibility and inclusivity through multilingual translation and assistive technologies. This abstract illuminates the transformative impact of AI in enhancing the efficiency, reach, and accessibility of scientific communication, heralding a more dynamic and collaborative era for research endeavors.

Keywords : AI-Driven Advancements, Scientific Knowledge Dissemination, Artificial Intelligence, Machine Learning, Natural Language Processing.

References :

  1. Achouch, Mounia, Mariya Dimitrova, Khaled Ziane, Sasan Sattarpanah Karganroudi, Rizck Dhouib, Hussein Ibrahim, and Mehdi Adda. 2022. “On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges.” Applied Sciences 12 (16): 8081.
  2. Cheng, Jack C. P., Weiwei Chen, Keyu Chen, and Qian Wang. 2020. “Data-Driven Predictive Maintenance Planning Framework for MEP Components Based on BIM and IoT Using Machine Learning Algorithms.” Automation in Construction 112 (April): 103087.
  3. Çınar, Zeki Murat, Abubakar Abdussalam Nuhu, Qasim Zeeshan, Orhan Korhan, Mohammed Asmael, and Babak Safaei. 2020. “Machine Learning in Predictive Maintenance Towards Sustainable Smart Manufacturing in Industry 4.0.” Sustainability 12 (19): 8211.
  4. Lee, Sang M., DonHee Lee, and Youn Sung Kim. 2019. “The Quality Management Ecosystem for Predictive Maintenance in the Industry 4.0 Era.” International Journal of Quality Innovation 5 (1).
  5. Liu, Yuehua, Wenjin Yu, Tharam Dillon, Wenny Rahayu, and Ming Li. 2022. “Empowering IoT Predictive Maintenance Solutions With AI: A Distributed System for Manufacturing Plant-Wide Monitoring.” IEEE Transactions on Industrial Informatics 18 (2): 1345–54.
  6. Massaro, Alessandro, Sergio Selicato, and Angelo Galiano. 2020. “Predictive Maintenance of Bus Fleet by Intelligent Smart Electronic Board Implementing Artificial Intelligence.” IoT 1 (2): 180–97.
  7. Pech, Martin, Jaroslav Vrchota, and Jiří Bednář. 2021. “Predictive Maintenance and Intelligent Sensors in Smart Factory: Review.” Sensors 21 (4): 1470.
  8. Yan, Jihong, Yue Meng, Lei Lu, and Lin Li. 2017. “Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance.” IEEE Access 5: 23484–91.

The integration of Artificial Intelligence (AI) has ushered in a new era in scientific knowledge dissemination. This abstract delineates the pivotal role played by AI technologies in revolutionizing the process of sharing and accessing scholarly information. By harnessing machine learning, natural language processing, and data analytics, AI facilitates automated literature reviews, intelligent summarization, and personalized content recommendations. Furthermore, it ensures greater accessibility and inclusivity through multilingual translation and assistive technologies. This abstract illuminates the transformative impact of AI in enhancing the efficiency, reach, and accessibility of scientific communication, heralding a more dynamic and collaborative era for research endeavors.

Keywords : AI-Driven Advancements, Scientific Knowledge Dissemination, Artificial Intelligence, Machine Learning, Natural Language Processing.

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