The AI powered enterprise: a review of cloud computing, web ‎technology and digital marketing

Authors

  • Harem Hadi

    Duhok polytechnic University, Technical college of informatics, Department of Information ‎Technology, Duhok, Kurdistan Region, Iraq

  • Subhi R. M. Zeebaree

    Energy Eng. Dept., Technical College of Engineering, Duhok Polytechnic University, Duhok, ‎Iraq

How to Cite

Hadi, H., & Zeebaree, S. R. M. . . (2025). The AI powered enterprise: a review of cloud computing, web ‎technology and digital marketing. International Journal of Scientific World, 11(1), 158-166. https://doi.org/10.14419/cevybm56

Received date: April 19, 2025

Accepted date: May 1, 2025

Published date: May 11, 2025

DOI:

https://doi.org/10.14419/cevybm56

Keywords:

AI Powered Enterprise; Cloud Computing; Web Technology; Digital Marketing

Abstract

The integration of artificial intelligence (AI) into enterprise systems is thoroughly reviewed in ‎this study, with an emphasis on cloud computing, online technologies, and digital marketing. ‎It demonstrates how AI-driven businesses use intelligent automation, real-time analytics, and ‎data-driven decision-making to dramatically increase operational productivity, scalability, and ‎customer engagement. A crucial piece of infrastructure, cloud computing provides the ‎scalable and adaptable resources required for large-scale data processing, AI implementation, ‎and strong cybersecurity. AI-enhanced web technologies, such as intelligent chatbots, ‎recommendation engines, and tailored content, improve user experiences and develop digital ‎interaction techniques. AI in digital marketing greatly improves client interactions and ‎satisfaction by enabling sentiment analysis, predictive analytics, accurate targeting, and ‎campaign automation. Prominent issues, including data privacy, ethical dilemmas, and biases ‎in AI systems, are also covered in the paper.

References

  1. Z. M. Khalid and S. R. M. Zeebaree, “Big Data Analysis for Data Visualization: A Review,” International Journal of Science and Business, vol. 5, no. 2, pp. 64-75, 2021, doi: 10.5281/zenodo.4481357.
  2. H. Dino et al., “Facial Expression Recognition Based on Hybrid Feature Extraction Techniques with Different Classifiers,” Test Engineering And Management, vol. 83, no. May-June 2020, pp. 22319-22329, 2020.
  3. R. K. Ibrahim et al., “Survey on Semantic Similarity Based on Document Clustering,” Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 115-122, 2019. https://doi.org/10.25046/aj040515.
  4. K. Jacksi, S. R. M. Zeebaree, and N. Dimililer, “LOD Explorer: Presenting the Web of Data,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 1, 2018, pp. 45-49. https://doi.org/10.14569/IJACSA.2018.090107.
  5. R. M. Zebari, S. and O. Yaseen, N. (2011), “Effects of Parallel Processing Implementation on Balanced Load-Division Depending on Distributed Memory Systems,” J. of University of Anbar for Pure Science, vol. 5, no. 3, 2011. https://doi.org/10.37652/juaps.2011.44313.
  6. J. Woolsey et al., “Designing a Novel Calculation Allocation Method Based on a Maximum Distance Separable Storage Assignment for Heteroge-neous Coded Elastic Computing Networks,” Drones, vol. 6, no. 337, 2022.
  7. MAHMOOD, Mayyadah R. et al., “Classification techniques’ performance evaluation for facial expression recognition”, Indonesian Journal of Electrical Engineering and Computer Science, vol. 21, no. 2, pp. 1176-1184, feb. 2021, https://doi.org/10.11591/ijeecs.v21.i2.pp1176-1184.
  8. A. T. Mehmood et al., “Big Data Analysis for Collecting Information in AI-Based Digital Marketing,” CUTLER Project, 2019.
  9. A. Ali et al., “Visualization and Parallel Computing Techniques for Big Data Processing,” IEEE Transactions on Big Data, vol. 6, no. 3, pp. 25-37, 2020.
  10. J. Carranza et al., “A Framework for Higher-Order Spectral Clustering and Typed Graph Behavior in Heterogeneous Networks,” Computational Intelligence and Neuroscience, vol. 2020, no. 4, pp. 25-37, 2020.
  11. Y. Jghef et al., “Bio-Inspired Dynamic Trust and Congestion-Aware Zone-Based Secured Internet of Drone Things (SIoDT),” Drones, vol. 6, no. 337, 2022, https://doi.org/10.3390/drones6110337.
  12. European Union, “General Data Protection Regulation (GDPR),” Official Journal of the European Union, 2018.
  13. P. Zhang et al., “Fairness and Bias in AI-Powered Decision-Making,” IEEE Transactions on Artificial Intelligence, vol. 2, no. 3, pp. 110-125, 2021.
  14. N. M. A., "AI in Enterprises: A Review," J. Smart Internet Things, vol. 2, no. 1, pp. 45-58, 2023.
  15. M. Shamal Salih et al., "Diabetic Prediction based on Machine Learning Using PIMA Indian Dataset," Communications on Applied Nonlinear Analysis, Vol 31, No. 5s, 2024, pp. 138–156, https://doi.org/10.52783/cana.v31.1008.
  16. A. Yazdeen et al., "Internet of Things Impact on Web Technology and Enterprise Systems," J. Appl. Sci. Technol. Trends (JASTT), vol. 5, no. 3, pp. 78-91, 2023.
  17. H. Malallah et al., "Performance Analysis of Enterprise Cloud Computing: A Review," JASTT, vol. 5, no. 2, pp. 33-48, 2023.
  18. P. Abdullah et al., "An hrm system for small and medium enterprises (sme) s based on cloud computing technology," International Journal of Re-search –GRANTHAALAYAH, August 2020, Vol 8(08), 56 – 64, https://doi.org/10.29121/granthaalayah.v8.i8.2020.926.
  19. S. Zeebaree et al., " Security Approaches for Integrated Enterprise Systems Performance: A Review," Int. J. Sci. Technol. Res. (IJSTR), vol. 8, no. 12, pp. 2485-2489, 2019.
  20. N. Salim et al., "Study for Food Recognition System Using Deep Learning," J. Phys.: Conf. Ser. (JPCS), vol. 1400, no. 2, pp. 78-91, 2021.
  21. K. Jacksi et al., "State of the Art Exploration Systems for Linked Data," Int. J. Adv. Comput. Sci. Appl. (IJACSA), vol. 7, no. 1, pp. 56-67, 2016. https://doi.org/10.14569/IJACSA.2016.071120.
  22. A. Khan et al., "IoT Based Smart Waste Bin to Track Dustbin," IEEE Conf. Smart Netw. Technol. (CSNT), vol. 6, pp. 332-340, 2018.
  23. J. Wang et al., "Design of a Smart Independent Smoke Sense System," IEEE Int. Conf. Inf. Technol. Big Data Secur. (ICITBS), vol. 7, pp. 220-235, 2019.
  24. https://jaxel.com/9-benefits-of-cloud-computing-everyone-needs-to-know/
  25. S. Kumar et al., "AI-Driven Cybersecurity in Cloud and Web Systems," IEEE Secur. Privacy, vol. 21, no. 2, pp. 45-59, 2024.
  26. D. Brown and C. Lee, "AI in Predictive Analytics for Digital Marketing," IEEE Trans. Mark. Sci., vol. 11, no. 2, pp. 78-94, 2023.
  27. R. Thomas, "Programmatic Advertising and AI: A New Approach," IEEE Trans. Consum. Electron., vol. 12, no. 1, pp. 101-115, 2024.
  28. T. Wang et al., "AI-Based Sentiment Analysis for Brand Management," IEEE Trans. NLP Sentiment Anal., vol. 10, no. 3, pp. 50-65, 2023.
  29. A. Johnson, "Data Privacy in AI-Driven Enterprises," IEEE Trans. Data Prot., vol. 8, no. 4, pp. 33-49, 2024.
  30. L. Chen et al., "Ethical AI: Bias and Transparency in Machine Learning," IEEE Trans. Ethics AI, vol. 6, no. 2, pp. 22-38, 2023.
  31. J. Park and K. Yamada, "Quantum Computing and AI: The Next Frontier," IEEE Trans. Quantum Comput., vol. 5, no. 1, pp. 5-20, 2024.
  32. G. White et al., "Federated Learning in AI Enterprises," IEEE Access, vol. 10, pp. 15432-15450, 2024.
  33. K. Zhao et al., "AI and Blockchain Integration for Secure Transactions," IEEE Blockchain Trans., vol. 8, no. 2, pp. 99-115, 2024.
  34. P. Gupta et al., "Edge AI in Smart Cities," IEEE Trans. Ind. Inform., vol. 15, no. 4, pp. 2201-2215, 2023.
  35. R. Patel et al., "AI in Financial Modeling: An Enterprise Perspective," IEEE Trans. Comput. Finance, vol. 9, no. 3, pp. 134-150, 2024.
  36. H. Lin et al., "Big Data Analytics and AI for Enterprise Decision Making," IEEE Trans. Big Data, vol. 12, no. 2, pp. 80-95, 2023.
  37. F. Ali et al., "Security Issues in AI-Powered Enterprises," IEEE Int. Conf. AI Cybersecur. (ICAI-CYBER), vol. 5, pp. 270-285, 2023.
  38. T. Nelson et al., "AI and Customer Engagement in Digital Marketing," IEEE Trans. Digit. Econ., vol. 7, no. 1, pp. 10-28, 2023.
  39. J. Smith et al., "AI and the Future of Cloud-Based Enterprise Solutions," IEEE Trans. Cloud AI Integr., vol. 11, no. 4, pp. 85-100, 2024.
  40. R. E. A. Armya, L. M. Abdulrahman, N. M. Abdulkareem, and A. A. Salih, “Web-based Efficiency of Distributed Systems and IoT on Functional-ity of Smart City Applications,” J. Smart Internet Things (JSIoT), vol. 2023, no. 2, pp. 142–161, Dec. 2023, https://doi.org/10.2478/jsiot-2023-0017.
  41. R. M. Abdullah, L. M. Abdulrahman, N. M. Abdulkareem, and A. A. Salih, “Modular Platforms based on Clouded Web Technology and Distrib-uted Deep Learning Systems,” J. Smart Internet Things (JSIoT), vol. 2023, no. 2, pp. 162–173, Dec. 2023, https://doi.org/10.2478/jsiot-2023-0018.
  42. N. M. Abdulkareem, A. M. Abdulazeez, D. Q. Zeebaree, and D. A. Hasan, “COVID-19 World Vaccination Progress Using Machine Learning Classification Algorithms,” Qalayet Acad. J., vol. 1, no. 2, 2021, https://doi.org/10.48161/qaj.v1n2a53.
  43. S. H. Haji, A. Al-zebari, A. Sengur, S. F. Kak, and N. M. Abdulkareem, “Document Clustering in the Age of Big Data: Incorporating Semantic Information for Improved Results,” J. Appl. Sci. Technol. Trends, vol. 4, no. 1, pp. 34–53, 2023, https://doi.org/10.38094/jastt401143.
  44. S. M. Mohammed, K. Jacksi, and S. R. M. Zeebaree, “Glove Word Embedding and DBSCAN Algorithms for Semantic Document Clustering,” in Proc. 3rd Int. Conf. Adv. Sci. Eng. (ICOASE 2020), Zakho, Iraq, 2020, pp. 211–216, https://doi.org/10.1109/ICOASE51841.2020.9436540.
  45. S. R. M. Zeebaree and K. Jacksi, “Effects of Processes Forcing on CPU and Total Execution-Time Using Multiprocessor Shared Memory System,” Int. J. Comput. Eng. Res. Trends, vol. 2, no. 4, pp. 275–279, Apr. 2015. [Online]. Available: http://www.ijcert.org.
  46. M. Armbrust et al., "A View of Cloud Computing," Communications of the ACM, vol. 53, no. 4, pp. 50–58, 2010. https://doi.org/10.1145/1721654.1721672.
  47. R. Buyya, J. Broberg, and A. M. Goscinski, Cloud Computing: Principles and Paradigms. Hoboken, NJ, USA: Wiley, 2011. https://doi.org/10.1002/9780470940105.
  48. Q. Zhang, L. Cheng, and R. Boutaba, "Cloud computing: state-of-the-art and research challenges," Journal of Internet Services and Applications, vol. 1, no. 1, pp. 7–18, 2010. https://doi.org/10.1007/s13174-010-0007-6.
  49. T. Khan, W. Tian, and R. Buyya, "Machine Learning (ML)-Centric Resource Management in Cloud Computing: A Review and Future Directions," Journal of Network and Computer Applications, vol. 176, pp. 1–23, 2021.
  50. S. S. Gill et al., "AI for Next Generation Computing: Emerging Trends and Future Directions," Internet of Things, vol. 19, Article 100514, 2022. https://doi.org/10.1016/j.iot.2022.100514.
  51. H. S. Talabani and I. H. Jumaa, "A Review of Various Machine Learning Techniques and Their Application on IoT and Cloud Computing," Tikrit Journal of Pure Science, vol. 29, no. 1, pp. 185–195, 2024. https://doi.org/10.25130/tjps.v29i1.1618.
  52. G. Zhou et al., "Deep Reinforcement Learning-Based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions," Concurrency and Computation: Practice and Experience, vol. 33, no. 19, e6387, 2021.
  53. D. Rosendo et al., "Distributed Intelligence on the Edge-to-Cloud Continuum: A Systematic Literature Review," IEEE Access, vol. 10, pp. 17116–17141, 2022.
  54. J. W. Rittinghouse and J. F. Ransome, Cloud Computing: Implementation, Management, and Security. Boca Raton, FL, USA: CRC Press, 2016. https://doi.org/10.1201/9781439806814.
  55. D. C. Marinescu, Cloud Computing: Theory and Practice. Cambridge, MA, USA: Morgan Kaufmann, 2017.
  56. E. Ebrahimi, M. Shamizanjani, and H. Tajvidi, "Using gamification to enhance user engagement and brand loyalty: A systematic review," Comput-ers in Human Behavior, vol. 106, pp. 1–18, 2020.
  57. O. Semenda, Y. Larina, and T. Kazmina, "Big Data Analytics and Social Media in Marketing Strategies," Procedia Computer Science, vol. 169, pp. 191–198, 2020.
  58. X. Wang, X. Lin, and X. Dang, "Supervised Learning in Spiking Neural Networks: A Review of Algorithms and Evaluations," Neural Networks, vol. 125, pp. 258–280, 2020. https://doi.org/10.1016/j.neunet.2020.02.011.
  59. C. Stergiou, K. E. Psannis, B. G. Kim, and B. Gupta, "Secure integration of IoT and cloud computing," Future Generation Computer Systems, vol. 78, pp. 964–975, 2018. https://doi.org/10.1016/j.future.2016.11.031.
  60. S. Deng, H. Zhao, W. Fang, J. Yin, S. Dustdar, and A. Y. Zomaya, "Edge Intelligence: The Confluence of Edge Computing and Artificial Intelli-gence," IEEE Internet of Things Journal, vol. 7, no. 8, pp. 7457–7469, 2020. https://doi.org/10.1109/JIOT.2020.2984887.
  61. W. Li and W. Chou, "Design patterns for web services in cloud computing," in Proc. IEEE International Conference on Services Computing (SCC), 2011, pp. 726–733.
  62. J. Yao, S. Zhang, and Y. Yao, "Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI," IEEE Transactions on Services Computing, vol. 15, no. 4, pp. 1961–1977, 2022.
  63. S. I. Ali, D. M. Abdulqader, O. M. Ahmed, H. R. Ismael, S. H. Ahmed, and L. Haji, "Consideration of Web Technology and Cloud Computing Inspiration for AI and IoT Role in Sustainable Decision-Making for Enterprise Systems," J. Inf. Technol. Informatics (JITI), vol. 3, no. 2, pp. 226-245, 2024.

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How to Cite

Hadi, H., & Zeebaree, S. R. M. . . (2025). The AI powered enterprise: a review of cloud computing, web ‎technology and digital marketing. International Journal of Scientific World, 11(1), 158-166. https://doi.org/10.14419/cevybm56

Received date: April 19, 2025

Accepted date: May 1, 2025

Published date: May 11, 2025