Main Article Content

Diptiben Ghelani


There are several unsuccessful IT initiatives in today's market among specialized small and medium-sized businesses due to a lack of control over the gap between the business and its goal. In other words, purchased products are not being sold, which is a regular occurrence in tourism retail businesses. These firms purchase a number of trip packages from large corporations, which then expire because to a lack of demand, resulting in a cost rather than an investment. To address this issue, we suggest detecting flaws that restrict a firm by re-engineering processes, allowing the creation of a business architecture based on emotional analysis, allowing small and medium-sized tourist companies (SMEs) to make better decisions and evaluate data. For more than a decade, business intelligence has been an important study subject in tourism, and with the arrival of big data, it has gotten even more attention. Big data summarises themes such as integrating large volumes of data from external data sources (e.g., online content), extracting information from any type of data source, particularly unstructured data (e.g., customer evaluations), and integrating data in real-time, as needed. For the tourist industry, business intelligence and big data are only beginning to reveal their full potential.. Because of the critical function and relevance of social media and online product evaluations in tourism, the aforementioned trends are becoming increasingly important for tourist businesses to maintain their competitiveness. More sophisticated IT systems, as well as new algorithms and methodologies, particularly in the areas of online content mining and text mining, open up new application domains for business intelligence approaches that have already attracted a lot of study.

Article Details

How to Cite


1. Mungoli, N. (2023). Adaptive Ensemble Learning: Boosting Model Performance through Intelligent Feature Fusion in Deep Neural Networks. arXiv preprint arXiv:2304.02653.
2. Mungoli, N. (2023). Adaptive Feature Fusion: Enhancing Generalization in Deep Learning Models. arXiv preprint arXiv:2304.03290.
3. Mungoli, N. (2023). Deciphering the Blockchain: A Comprehensive Analysis of Bitcoin's Evolution, Adoption, and Future Implications. arXiv preprint arXiv:2304.02655.
4. Mungoli, N. (2023). Scalable, Distributed AI Frameworks: Leveraging Cloud Computing for Enhanced Deep Learning Performance and Efficiency. arXiv preprint arXiv:2304.13738.
5. Mungoli, N. (2020). Exploring the Technological Benefits of VR in Physical Fitness (Doctoral dissertation, The University of North Carolina at Charlotte).
6. Mahmood, T., Fulmer, W., Mungoli, N., Huang, J., & Lu, A. (2019, October). Improving information sharing and collaborative analysis for remote geospatial visualization using mixed reality. In 2019 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (pp. 236-247). IEEE.
7. Mughal, A. A. (2018). Artificial Intelligence in Information Security: Exploring the Advantages, Challenges, and Future Directions. Journal of Artificial Intelligence and Machine Learning in Management, 2(1), 22-34.
8. Mughal, A. A. (2018). The Art of Cybersecurity: Defense in Depth Strategy for Robust Protection. International Journal of Intelligent Automation and Computing, 1(1), 1-20.
9. Mughal, A. A. (2019). Cybersecurity Hygiene in the Era of Internet of Things (IoT): Best Practices and Challenges. Applied Research in Artificial Intelligence and Cloud Computing, 2(1), 1-31.
10. Mughal, A. A. (2020). Cyber Attacks on OSI Layers: Understanding the Threat Landscape. Journal of Humanities and Applied Science Research, 3(1), 1-18.
11. Mughal, A. A. (2019). A COMPREHENSIVE STUDY OF PRACTICAL TECHNIQUES AND METHODOLOGIES IN INCIDENT-BASED APPROACHES FOR CYBER FORENSICS. Tensorgate Journal of Sustainable Technology and Infrastructure for Developing Countries, 2(1), 1-18.
12. Mughal, A. A. (2022). Building and Securing the Modern Security Operations Center (SOC). International Journal of Business Intelligence and Big Data Analytics, 5(1), 1-15.
13. Mughal, A. A. (2022). Well-Architected Wireless Network Security. Journal of Humanities and Applied Science Research, 5(1), 32-42.
14. Mughal, A. A. (2021). Cybersecurity Architecture for the Cloud: Protecting Network in a Virtual Environment. International Journal of Intelligent Automation and Computing, 4(1), 35-48.
15. Bharadiya, J. P. (2023). A Comprehensive Survey of Deep Learning Techniques Natural Language Processing. European Journal of Technology, 7(1), 58 - 66.
16. "Bharadiya, J. P. (2023). Machine Learning in Cybersecurity: Techniques and Challenges. European Journal of Technology, 7(2), 1 - 14. "
17. Bharadiya, J. P. (2023). Artificial Intelligence in Transportation Systems A Critical Review. American Journal of Computing and Engineering, 6(1), 34 - 45.
18. "Bharadiya, J. P. (2023). The Impact of Artificial Intelligence on Business Processes. European Journal of Technology, 7(2), 15 - 25. "
19. "Bharadiya, J. P. (2023). Transfer Learning in Natural Language Processing (NLP). European Journal of Technology, 7(2), 26 - 35. "
20. Bharadiya, J. P. (2023). Convolutional Neural Networks for Image Classification. International Journal of Innovative Science and Research Technology, 8(5), 673 - 677.
21. "Bharadiya, J. P. (2023, May). Exploring the Use of Recurrent Neural Networks for Time Series Forecasting. International Journal of Innovative Science and Research Technology, 8(5), 2023-2027. DOI: "
22. Bharadiya, J. P. (2023, May). A Review of Bayesian Machine Learning Principles, Methods, and Applications. International Journal of Innovative Science and Research Technology, 8(5), 2033-2038. DOI:
23. "Bharadiya, J. P. (2023, May). A Tutorial on Principal Component Analysis for Dimensionality Reduction in Machine Learning. International Journal of Innovative Science and Research Technology, 8(5), 2028-2032. DOI:
24. Kilanko, V. The Potential Effects of Biden’s Infrastructure Bill on the American Economy.
25. Kilanko, V. (2023). Government Response and Perspective on Autonomous Vehicles. In Government Response to Disruptive Innovation: Perspectives and Examinations (pp. 137-153). IGI Global.
26. Kilanko, V. (2022). Turning Point: Policymaking in the Era of Artificial Intelligence, by Darrell M. West and John R. Allen, Washington, DC: Brookings Institution Press, 2020, 297 pp., hardcover 24.99,paperback 19.99.
27. Kilanko, V. The Transformative Potential of Artificial Intelligence in Medical Billing: A Global Perspective.
28. Khan, M. S., & Minhaj, S. A. (2021). Numerical Analysis Of De Laval Nozzle Under Surrounding Zone and Compressed Flow. International Journal for Research in Applied Science and Engineering Technology, 9(1), 98-105.
29. Nallamothu, P. T., & Khan, M. S. (2023). Machine Learning for SPAM Detection. Asian Journal of Advances in Research, 167-179.
30. Nallamothu, P. T., & Khan, M. S. (2023). Machine Learning for SPAM Detection. Asian Journal of Advances in Research, 167-179.
31. Chaudhary, J. K., Sharma, H., Tadiboina, S. N., Singh, R., Khan, M. S., & Garg, A. (2023, March). Applications of Machine Learning in Viral Disease Diagnosis. In 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 1167-1172). IEEE.
32. Khan, M. S. Control of Autonomous License Plate Recognition Drone in GPS Denied Parking Lot.
33. Latha, K. H., Khan, K. A., Minhaj, S. A., & Khan, M. S. Design and Fatigue Analysis of Shot Peened Leaf Spring.