The Impact of Artificial Intelligence (AI) on Content Management Systems (CMS): A Deep Dive
Keywords:
Artificial Intelligence (AI), Content Management System (CMS), Natural Language Processing(NLP), Machine Learning(ML), Automation, Digital ContentAbstract
The dynamic nature of the global environment is always changing, with technology playing a pivotal role in propelling these shifts. The emergence of artificial intelligence (AI) has fundamentally transformed the manner in which we oversee and engage with our digital data. The potential of integrating artificial intelligence (AI) with content management systems (CMS) holds significant promise for future advancements. Artificial intelligence (AI) has the potential to bring about substantial changes in the manner in which information is managed and shared on the internet. It can enhance search functionalities and streamline numerous processes via automation. Individuals engaged in website ownership, content generation, and marketing are required to acquaint themselves with the most recent advancements in content management systems (CMS) and artificial intelligence (AI). The objective of this article is to provide a comprehensive examination of the influence of artificial intelligence (AI) on content management systems (CMS), along with an analysis of emerging AI methodologies and their practical use within a corporate environment.
Downloads
References
K. Li and N. Gui, “CMS: A Continuous Machine-Learning and Serving Platform for Industrial Big Data,” Future Internet, vol. 12, no. 6, p. 102, Jun. 2020, doi: https://doi.org/10.3390/fi12060102.
P. Bellavista, J. Berrocal, A. Corradi, S. K. Das, L. Foschini, and A. Zanni, “A survey on fog computing for the Internet of Things,” Pervasive and Mobile Computing, vol. 52, pp. 71–99, Jan. 2019, doi: https://doi.org/10.1016/j.pmcj.2018.12.007.
“Content Management and Artificial Intelligence: The Future of Content Ops,” www.contentstack.com. https://www.contentstack.com/blog/all-about-headless/content-management-artificial-intelligence-content-ops (accessed Oct. 01, 2023).
Moussa Mahamat Boukar and I. Muslu, “Administration and academic staff performance management system using content management system (CMS) technologies,” International Conference on Electronics, Computer and Computation, Nov. 2013, doi: https://doi.org/10.1109/icecco.2013.6718251.
Ahmed Dalhatu Yusuf, Moussa Mahamat Boukar, and Shahriar Shamiluulu, “Automated batch certificate generation and verification system,” Nov. 2017, doi: https://doi.org/10.1109/icecco.2017.8333321.
W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge Computing: Vision and Challenges,” IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, Oct. 2016, doi: https://doi.org/10.1109/jiot.2016.2579198.
J. Lee, H. D. Ardakani, S. Yang, and B. Bagheri, “Industrial Big Data Analytics and Cyber-physical Systems for Future Maintenance & Service Innovation,” Procedia CIRP, vol. 38, pp. 3–7, 2015, doi: https://doi.org/10.1016/j.procir.2015.08.026.
J. Wan et al., “A Manufacturing Big Data Solution for Active Preventive Maintenance,” IEEE Transactions on Industrial Informatics, vol. 13, no. 4, pp. 2039–2047, Aug. 2017, doi: https://doi.org/10.1109/tii.2017.2670505.
U. A. Ibrahim, M. M. Boukar, and M. A. Suleiman, “Development of Hausa dataset a baseline for speech recognition,” Data in Brief, vol. 40, p. 107820, Feb. 2022, doi: https://doi.org/10.1016/j.dib.2022.107820.
S. Landset, T. M. Khoshgoftaar, A. N. Richter, and T. Hasanin, “A survey of open source tools for machine learning with big data in the Hadoop ecosystem,” Journal of Big Data, vol. 2, no. 1, Nov. 2015, doi: https://doi.org/10.1186/s40537-015-0032-1.
Y. Zhu, J. Ma, B. An, and D. Cao, “Monitoring and Billing of A Lightweight Cloud System Based on Linux Container,” Jun. 2017, doi: https://doi.org/10.1109/icdcsw.2017.73.
A. Benveniste et al., “Contracts for System Design,” Foundations and Trends® in Electronic Design Automation, vol. 12, no. 2–3, pp. 124–400, 2018, doi: https://doi.org/10.1561/1000000053.
U. B. Abubakar, M. M. Boukar, and S. Adeshina, “Evaluation of Parameter Fine-Tuning with Transfer Learning for Osteoporosis Classification in Knee Radiograph,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 8, 2022, doi: https://doi.org/10.14569/ijacsa.2022.0130829.
K.-S. Lee, S.-K. Jung, J.-J. Ryu, S.-W. Shin, and J. Choi, “Evaluation of Transfer Learning with Deep Convolutional Neural Networks for Screening Osteoporosis in Dental Panoramic Radiographs,” Journal of Clinical Medicine, vol. 9, no. 2, Feb. 2020, doi: https://doi.org/10.3390/jcm9020392.
R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artificial Intelligence, vol. 97, no. 1–2, pp. 273–324, Dec. 1997, doi: https://doi.org/10.1016/s0004-3702(97)00043-x.
“The future of content management systems with AI,” AIContentfy, Jan. 31, 2023. https://aicontentfy.com/en/blog/future-of-content-management-systems-with-ai (accessed Oct. 02, 2023).
K. C. H. Tsang, H. Pinnock, A. M. Wilson, and S. Ahmar Shah, “Application of Machine Learning to Support Self-Management of Asthma with mHealth,” IEEE Xplore, Jul. 01, 2020. https://ieeexplore.ieee.org/document/9175679/
Satya Murthy Sasubilli, A. Kumar, and V. Dutt, “Machine Learning Implementation on Medical Domain to Identify Disease Insights using TMS,” Jun. 2020, doi: https://doi.org/10.1109/icacce49060.2020.9154960.
M. Bernardini, L. Romeo, E. Frontoni, and M.-R. Amini, “A Semi-Supervised Multi-Task Learning Approach for Predicting Short-Term Kidney Disease Evolution,” IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 10, pp. 3983–3994, Oct. 2021, doi: https://doi.org/10.1109/jbhi.2021.3074206.
R. Jahan and M. M. Tripathi, “Brain Tumor Detection Using Machine Learning in MR Images,” 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), Jun. 2021, doi: https://doi.org/10.1109/csnt51715.2021.9509695.
C. Fu, H. Jiang, and X. Chen, “Modeling of an Enterprise Knowledge Management System Based on Artificial Intelligence,” Knowledge Management Research & Practice, pp. 1–13, Jan. 2022, doi: https://doi.org/10.1080/14778238.2020.1854632.
R. Amin and Mounika Mandapuram, “CMS - Intelligent Machine Translation with Adaptation and AI,” ABC journal of advanced research, vol. 10, no. 2, pp. 199–206, Dec. 2021, doi: https://doi.org/10.18034/abcjar.v10i2.693.
A. I., A. E., and O. M., “Adopting Cloud Computing Technology to Enhance the Architecture of a Proposed Learning Content Management System,” International Journal of Computer Applications, vol. 153, no. 6, pp. 20–27, Nov. 2016, doi: https://doi.org/10.5120/ijca2016912077.
H. Sun and W. Ni, “Design and Application of an AI-Based Text Content Moderation System,” Scientific Programming, vol. 2022, pp. 1–9, Feb. 2022, doi: https://doi.org/10.1155/2022/2576535.
R. Manhiça, A. Santos, and J. Cravino, “The use of artificial intelligence in learning management systems in the context of higher education: Systematic literature review,” IEEE Xplore, Jun. 01, 2022. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9820205
M. Mandapuram, S. S. Gutlapalli, M. Reddy, and A. Bodepudi, “Application of Artificial Intelligence (AI) Technologies to Accelerate Market Segmentation,” Global Disclosure of Economics and Business, vol. 9, no. 2, pp. 141–150, Dec. 2020, doi: https://doi.org/10.18034/gdeb.v9i2.662.
H. R. Nemati, D. M. Steiger, L. S. Iyer, and R. T. Herschel, “Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing,” Decision Support Systems, vol. 33, no. 2, pp. 143–161, Jun. 2002, doi: https://doi.org/10.1016/s0167-9236(01)00141-5.
S. Cazalens, P. Lamarre, J. Leblay, I. Manolescu, and X. Tannier, “A Content Management Perspective on Fact-Checking,” Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW ’18, 2018, doi: https://doi.org/10.1145/3184558.3188727.
Y. Sharrab, N. T. Almutiri, M. Tarawneh, F. Alzyoud, A.-R. F. Al-Ghuwairi, and D. Al-Fraihat, “Toward Smart and Immersive Classroom based on AI, VR, and 6G,” International Journal of Emerging Technologies in Learning (iJET), vol. 18, no. 02, pp. 4–16, Jan. 2023, doi: https://doi.org/10.3991/ijet.v18i02.35997.
K.-Y. Tang, C.-Y. Chang, and G.-J. Hwang, “Trends in artificial intelligence-supported e-learning: a systematic review and co-citation network analysis (1998–2019),” Interactive Learning Environments, pp. 1–19, Jan. 2021, doi: https://doi.org/10.1080/10494820.2021.1875001.
Arjun Reddy Kunduru, “Industry Best Practices on Implementing Oracle Cloud ERP Security,” International Journal of Computer Trends and Technology, vol. 71, no. 6, pp. 1–8, Jun. 2023, doi: https://doi.org/10.14445/22312803/ijctt-v71i6p101.
Arjun Reddy Kunduru, “Effective Usage of Artificial Intelligence in Enterprise Resource Planning Applications,” vol. 71, no. 4, pp. 73–80, Apr. 2023, doi: https://doi.org/10.14445/22312803/ijctt-v71i4p109.
A. R. Kunduru and R. Kandepu, “Data Archival Methodology in Enterprise Resource Planning Applications (Oracle ERP, Peoplesoft),” Journal of Advances in Mathematics and Computer Science, vol. 38, no. 9, pp. 115–127, Aug. 2023, doi: https://doi.org/10.9734/jamcs/2023/v38i91809.
G. George, S. Adeshina, and M. M. Boukar, “Development of Android Application for Facial Age Group Classification Using TensorFlow Lite,” International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 4, pp. 11–17, Sep. 2023, Accessed: Sep. 24, 2023. [Online]. Available: https://ijisae.org/index.php/IJISAE/article/view/3449
Aishat Salau, N. Agwu, and Moussa Prof. Moussa, “Deep Learning for Fraud Prediction in Preauthorization for Health Insurance,” International journal of engineering and advanced technology, vol. 12, no. 2, pp. 75–81, Dec. 2022, doi: https://doi.org/10.35940/ijeat.b3915.1212222.
“Generative AI Models Explained,” AltexSoft, Oct. 13, 2022. https://www.altexsoft.com/blog/generative-ai/
“The demonstration of graph learning based recommender systems - Recherche Google,” www.google.com. https://www.google.com/search?q=The+demonstration+of+graph+learning+based+recommender+systems&source=lmns&bih=965&biw=1680&hl=fr&sa=X&ved=2ahUKEwjrzK28k-6BAxUDpycCHaacDksQ_AUoAHoECAEQAA (accessed Oct. 11, 2023).
N. C. Kundur and P. B. Mallikarjuna, “Deep Convolutional Neural Network Architecture for Plant Seedling Classification,” Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9464–9470, Dec. 2022, doi: https://doi.org/10.48084/etasr.5282.
D. Patil and S. Jadhav, “Road Segmentation in High-Resolution Images Using Deep Residual Networks,” Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9654–9660, Dec. 2022, doi: https://doi.org/10.48084/etasr.5247.
Pathan Ilius, M. Al-Muhaini, M. Javaid, and M. A. Abido, “A Machine Learning–Based Approach for Fault Detection in Power Systems,” Engineering, Technology & Applied Science Research, vol. 13, no. 4, pp. 11216–11221, Aug. 2023, doi: https://doi.org/10.48084/etasr.5995.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
