Machine Learning and AI in Business Intelligence: Trends and Opportunities
Keywords:
artificial intelligence, Machine learning, sentiment analysis, student reviews, deep learning, online coursesAbstract
The integration of machine learning and artificial intelligence (AI) in business intelligence has brought forth a plethora of trends and opportunities. These cutting-edge technologies have revolutionized how businesses analyze data, gain insights, and make informed decisions. One prominent trend is the rise of predictive analytics. Machine learning algorithms can sift through vast amounts of historical data to identify patterns and trends, enabling businesses to make accurate predictions about future outcomes. This empowers organizations to optimize operations, anticipate customer needs, and mitigate risks. By leveraging business intelligence, companies can uncover hidden patterns, identify opportunities for growth and improvement, optimize business processes, and ultimately make informed decisions that drive their success. Another trend is the adoption of AI-powered chatbots and virtual assistants. The opportunities presented by machine learning and AI in business intelligence are extensive. From automated data analysis and anomaly detection to demand forecasting and dynamic pricing, these technologies empower businesses to optimize processes, reduce costs, and identify new revenue streams. In conclusion, the integration of machine learning and AI in business intelligence offers promising trends and abundant opportunities. By leveraging these technologies, businesses can gain a competitive edge, drive innovation, and unlock new levels of success in the digital era.
References
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.
Kilanko, V. The Transformative Potential of Artificial Intelligence in Medical Billing: A Global Perspective.
Mungoli, N. (2023). Adaptive Ensemble Learning: Boosting Model Performance through Intelligent Feature Fusion in Deep Neural Networks. arXiv preprint arXiv:2304.02653.
Mungoli, N. (2023). Adaptive Feature Fusion: Enhancing Generalization in Deep Learning Models. arXiv preprint arXiv:2304.03290.
Mungoli, N. (2023). Deciphering the Blockchain: A Comprehensive Analysis of Bitcoin's Evolution, Adoption, and Future Implications. arXiv preprint arXiv:2304.02655.
Sahija, D. (2021). Critical review of machine learning integration with augmented reality for discrete manufacturing. Independent Researcher and Enterprise Solution Manager in Leading Digital Transformation Agency, Plano, USA.
Sahija, D. (2021). User Adoption of Augmented Reality and Mixed Reality Technology in Manufacturing Industry. Int J Innov Res Multidisciplinary Field Issue, 27, 128-139.
Mungoli, N. (2023). Scalable, Distributed AI Frameworks: Leveraging Cloud Computing for Enhanced Deep Learning Performance and Efficiency. arXiv preprint arXiv:2304.13738.
Mungoli, N. (2020). Exploring the Technological Benefits of VR in Physical Fitness (Doctoral dissertation, The University of North Carolina at Charlotte).
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.
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.
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.
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.
Mughal, A. A. (2020). Cyber Attacks on OSI Layers: Understanding the Threat Landscape. Journal of Humanities and Applied Science Research, 3(1), 1-18.
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.
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.
Mughal, A. A. (2022). Well-Architected Wireless Network Security. Journal of Humanities and Applied Science Research, 5(1), 32-42.
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.
Azim, A. Bazzi, R. Shubair and M. Chafii, "Dual-Mode Chirp Spread Spectrum Modulation," in IEEE Wireless Communications Letters, vol. 11, no. 9, pp. 1995-1999, Sept. 2022, doi: 10.1109/LWC.2022.3190564.
W. Njima, A. Bazzi and M. Chafii, "DNN-Based Indoor Localization Under Limited Dataset Using GANs and Semi-Supervised Learning," in IEEE Access, vol. 10, pp. 6989669909, 2022, doi: 10.1109/ACCESS.2022.3187837.
Azim, A.W., Bazzi, A., Shubair, R. and Chafii, M., 2022. A Survey on Chirp Spread Spectrum-based Waveform Design for IoT. arXiv preprint arXiv:2208.10274.
Azim, A. W., Bazzi, A., Fatima, M., Shubair, R., & Chafii, M. (2022). Dual-Mode Time Domain Multiplexed Chirp Spread Spectrum. arXiv preprint arXiv:2210.04094.
Bazzi, A. and M. Chafii, "On Integrated Sensing and Communication Waveforms with Tunable PAPR," in IEEE Transactions on Wireless Communications, doi: 10.1109/TWC.2023.3250263.
Jasmin Praful Bharadiya. Artificial Intelligence and the Future of Web 3.0: Opportunities and Challenges Ahead.
Sestino, A., & De Mauro, A. (2022). Leveraging artificial intelligence in business: Implications, applications and methods. Technology Analysis & Strategic Management, 34(1), 16-29.
Colace, F., De Santo, M., Lombardi, M., Mercorio, F., Mezzanzanica, M., & Pascale, F. (2019). Towards labour market intelligence through topic modelling.
Reshi, Y. S., & Khan, R. A. (2014). Creating business intelligence through machine learning: An Effective business decision making tool. In Information and Knowledge Management (Vol. 4, No. 1, pp. 65-75).
Jasmin Praful Bharadiya, The Future of Cybersecurity: How artificial Intelligence Will Transform the Industry.
Kuleto, V., Ili?, M., Dumangiu, M., Rankovi?, M., Martins, O. M., P?un, D., & Mihoreanu, L. (2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability, 13(18), 10424.
Jasmin Praful Bharadiya. (2023). Convolutional Neural Networks for Image Classification. International Journal of Innovative Science and Research Technology, 8(5), 673–677. https://doi.org/10.5281/zenodo.7952031
Kuleto, V., Ili?, M., Dumangiu, M., Rankovi?, M., Martins, O. M., P?un, D., & Mihoreanu, L. (2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability, 13(18), 10424.
Nallamothu, P. T., & Bharadiya, J. P. (2023). Artificial Intelligence in Orthopedics: A Concise Review. Asian Journal of Orthopaedic Research, 9(1), 17–27. Retrieved from https://journalajorr.com/index.php/AJORR/article/view/164.
Bharadiya, J. . (2023). A Comprehensive Survey of Deep Learning Techniques Natural Language Processing. European Journal of Technology, 7(1), 58 - 66. https://doi.org/10.47672/ejt.1473
Strusani, D., & Houngbonon, G. V. (2019). The role of artificial intelligence in supporting development in emerging markets.
Bharadiya , J. P., Tzenios, N. T., & Reddy , M. (2023). Forecasting of Crop Yield using Remote Sensing Data, Agrarian Factors and Machine Learning Approaches. Journal of Engineering Research and Reports, 24(12), 29–44. https://doi.org/10.9734/jerr/2023/v24i12858.
Sun, Z., Sun, L., & Strang, K. (2018). Big data analytics services for enhancing business intelligence. Journal of Computer Information Systems, 58(2), 162-169.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 1165-1188.
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